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Rozprawy doktorskie na temat „Object matching”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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Lennartsson, Mattias. "Object Recognition with Cluster Matching." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51494.

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Within this thesis an algorithm for object recognition called Cluster Matching has been developed, implemented and evaluated. The image information is sampled at arbitrary sample points, instead of interest points, and local image features are extracted. These sample points are used as a compact representation of the image data and can quickly be searched for prior known objects. The algorithm is evaluated on a test set of images and the result is surprisingly reliable and time efficient.

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Ahn, Yushin. "Object space matching and reconstruction using multiple images." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1213375997.

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Anderson, R. "Phase-based object matching using complex wavelets." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595514.

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This thesis investigates the use of phase information from the Dual-Tree Complex Wavelet Transform (DT CWT) for the purpose of image matching. We review methods for image matching with a particular emphasis on matching with local features. We review the meaning and current uses of local phase, and introduce the DT CWT. We highlight the shortcomings of decimated wavelets for typical local phase analysis, and introduce two new functions that can extract useful information from the phases of decimated complex wavelets. The first, the InterLevel Product (ILP), is a pyramidal representation where the phases are equal to the phase difference between co-located, same-direction DT CWT coefficients at different scales. The second function, the Same-Level Product (SLP), has phases proportional to the phase differences between adjacent coefficients in the same level; these SLP phases are parallel to the gradient of dominant local features. We seek to represent dominant local features sparsely by clustering areas where the ILP phase is large and same-phase. Three novel clustering techniques are introduced and discussed, with the Line-Growing technique shown to be the best. Line-Growing is a technique similar to the Canny edge detector, operating upon decimated coefficients and sensitive to phase symmetry changes. We call the resulting clusters Edge-Profile Clusters (EPC’s). We explore three different matching techniques based upon local phase information. The first technique shows how ILP information can be combined with the normalized cross correlation (template matching) to accelerate traditional matching. We also show a hybrid ILP/SLP format, where the target is abstracted into EPC cluster constellations that may be rotated quickly to test different match hypotheses. Finally, we show a method where EPC parameters are used to represent both images in the comparison, and a geometric hashing algorithm is combined with a cluster overlap metric to evaluated matches in a fully affine-invariant manner. We highlight the compatibility of our EPC and ILP representations and current physiological/psychovisual observations regarding the mammalian visual cortex, including an ILP phase-based explanation of the “pop-out” effect and perceptual grouping regarding “Glass patterns”.

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Kwon, Ohkyu. "Similarity measures for object matching in computer vision." Thesis, University of Bolton, 2016. http://ubir.bolton.ac.uk/890/.

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The similarity measures for object matching and their applications have been important topics in many fields of computer vision such as those of image recognition, image fusion, image analysis, video sequence matching, and so on. This critical commentary presents the efficiency of new metric methods such as the robust Hausdorff distance (RHD), the accurate M-Hausdorff distance (AMHD), and the fast sum of absolute differences (FSAD). The RHD measure computes the similarity distance of the occluded/noisy image pair and evaluates the performances of the multi-modal registration algorithms. The AMHD measure is utilised for aligning the pair of the occluded/noisy multi-sensor face images, and the FSAD measure in adaptive-template matching method finds the zero location of the slide in an automatic scanning microscope system. A Hausdorff distance (HD) similarity measure has been widely investigated to compare the pair of two-dimensional (2-D) images by low-level features since it is simple and insensitive to the changes in an image characteristic. In this research, novel HD measures based on the robust statistics of regression analysis are addressed for occluded and noisy object matching, resulting in two RHD measures such as M-HD based on the M-estimation and LTS-HD based on the least trimmed squares (LTS). The M-HD is extended to three-dimensional (3-D) version for scoring the registration algorithms of the multi-modal medical images. This 3-D measure yields the comparison results with different outlier-suppression parameters (OSP) quantitatively, even though the Computed Tomography (CT) and emission-Positron Emission Tomography (PET) images have different distinctive features. The RHD matching technique requires a high level of complexity in computing the minimum distance from one point to the nearest point between two edge point sets and searching for the best fit of matching position. To overcome these problems, the improved 3×3 distance transform (DT) is employed. It has a separable scan structure to reduce the calculation time of the minimum distance in multi-core processors. The object matching algorithm with hierarchical structures is also demonstrated to minimize the computational complexity dramatically without failing the matching position. The object comparison between different modality images is still challenging due to the poor edge correspondence coming from heterogeneous characteristics. To improve the robustness of HD measures in comparing the pair of multi-modal sensor images, an accurate M-HD (AMHD) is proposed by utilizing the orientation information of each point in addition to the DT map. This similarity measure can precisely analyse the non-correspondent edges and noises by using the distance orientation information. The AMHD measure yields superior performance at aligning the pairs of multi-modal face images over those achieved by the conventional robust HD schemes. The sum of absolute differences (SAD) is popular similarity measure in template matching technique. This thesis shows the adaptive-template matching method based on the FSAD for accurately locating the slide in automated microscope. The adaptive-template matching method detects the fiduciary ring mark in the slide by predicting the constant used in the template, where the FSAD reduces the processing time with a low rate of error of the template matching by inducing 1-D vertical and horizontal SAD. The proposed scheme results in an accurate performance in terms of detecting the ring mark and estimating the relative offset in slide alignment during the on-line calibration process.

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Tieu, Kinh H. (Kinh Han) 1976. "Statistical dependence estimation for object interaction and matching." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38316.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. Includes bibliographical references (p. 97-103). This dissertation shows how statistical dependence estimation underlies two key problems in visual surveillance and wide-area tracking. The first problem is to detect and describe interactions between moving objects. The goal is to measure the influence objects exert on one another. The second problem is to match objects between non-overlapping cameras. There, the goal is to pair the departures in one camera with the arrivals in a different camera so that the resulting distribution of relationships best models the data. Both problems have become important for scaling up surveillance systems to larger areas and expanding the monitoring to more interesting behaviors. We show how statistical dependence estimation generalizes previous work and may have applications in other areas. The two problems represent different applications of our thesis that statistical dependence estimation underlies the learning of the structure of probabilistic models. First, we analyze the relationship between Bayesian, information-theoretic, and classical statistical methods for statistical dependence estimation. Then, we apply these ideas to formulate object interaction in terms of dependency structure model selection. (cont.) We describe experiments on simulated and real interaction data to validate our approach. Second, we formulate the matching problem in terms of maximizing statistical dependence. This allows us to generalize previous work on matching, and we show improved results on simulated and real data for non-overlapping cameras. We also prove an intractability result on exact maximally dependent matching. by Kinh Tieu. Ph.D.

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Ko, Kwang Hee 1971. "Algorithms for three-dimensional free-form object matching." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29751.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 2003. Includes bibliographical references (leaves 117-126). This thesis addresses problems of free-form object matching for the point vs. NURBS surface and the NURBS surface vs. NURBS surface cases, and its application to copyright protection. Two new methods are developed to solve a global and partial matching problem with no a priori information on correspondence or initial transformation and no scaling effects, namely the KH and the umbilic method. The KH method establishes a correspondence between two objects by utilizing the Gaussian and mean curvatures. The umbilic method uses the qualitative properties of umbilical points to find correspondence information between two objects. These two methods are extended to deal with uniform scaling effects. The umbilic method is enhanced with an algorithm for scaling factor estimation using the quantitative properties of umbilical points. The KH method is used as a building block of an optimization scheme based on the golden section search which recovers iteratively an optimum scaling factor. Since the golden section search only requires an initial interval for the scaling factor, the solution process is simplified compared to iterative optimization algorithms, which require good initial estimates of the scaling factor and the rigid body transformation. The matching algorithms are applied to problems of copyright protection. (cont.) A suspect model is aligned to an original model through matching methods so that similarity between two geometric models can be assessed to determine if the suspect model contains part(s) of the original model. Three types of tests, the weak, intermediate and strong tests, are proposed for similarity assessment between two objects. The weak and intermediate tests are performed at node points obtained through shape intrinsic wireframing. The strong test relies on isolated umbilical points which can be used as fingerprints of an object for supporting an ownership claim to the original model. The three tests are organized in two decision algorithms so that they produce systematic and statistical measures for a similarity decision between two objects in a hierarchical manner. Based on the systematic statistical evaluation of similarity, a decision can be reached whether the suspect model is a copy of the original model. by Kwang Hee Ko. Ph.D.

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Sangi, P. (Pekka). "Object motion estimation using block matching with uncertainty analysis." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526200774.

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Abstract Estimation of 2-D motion is one of the fundamental problems in video processing and computer vision. This thesis addresses two general tasks in estimating projected motions of background and foreground objects in a scene: global motion estimation and motion based segmentation. The work concentrates on the study of the block matching method, and especially on those cases where the matching measure is based on the sum of squared or absolute displaced frame differences. Related techniques for performing the confidence analysis of local displacement are considered and used to improve the performance of the higher-level tasks mentioned. In general, local motion estimation techniques suffer from the aperture problem. Therefore, confidence analysis methods are needed which can complement motion estimates with information about their reliability. This work studies a particular form of confidence analysis which uses the evaluation of the match criterion for local displacement candidates. In contrast to the existing approaches, the method takes into account the local image gradient. The second part of the thesis presents a four-step feature based method for global motion estimation. For basic observations, it uses motion features which are combinations of image point coordinates, displacement estimates at those points, and representations of displacement uncertainty. A parametric form of uncertainty representation is computed exploiting the technique described in the first part of the thesis. This confidence information is used as a basis for weighting the features in motion estimation. Aspects of gradient based feature point selection are also studied. In the experimental part, the design choices of the method are compared, using both synthetic and real sequences. In the third part of the thesis, a technique for feature based extraction of background and foreground motions is presented. The new sparse segmentation algorithm performs competitive segmentation using both the spatial and temporal propagation of support information. The weighting of features exploits parametric uncertainty information which is experimentally shown to improve the performance of motion estimation. In the final part of the thesis, a novel framework for motion based object detection, segmentation, and tracking is developed. It uses a block grid based representation for segmentation and a particle filter based approach to motion estimation. Analysis techniques for obtaining the segmentation are described. Finally, the approach is integrated with the sparse motion segmentation and the combination of the methods is experimentally shown to increase both the efficiency of sampling and the accuracy of segmentation Tiivistelmä Tässä väitöskirjassa tutkitaan yhtä videonkäsittelyn ja konenäön perusongelmaa, kaksiulotteisen liikkeen estimointia. Työ käsittelee kahta yleistä tehtävää taustan ja etualan kohteiden liikkeiden määrittämisessä: hallitsevan liikkeen estimointia ja liikepohjaista kuvan segmentointia. Tutkituissa ratkaisuissa lähtökohtana käytetään lohkosovitukseen perustuvaa paikallisen liikkeen määritystä, jossa sovituksen kriteerinä käytetään poikkeutettujen kehysten pikseliarvojen erotusta. Tähän liittyen tarkastellaan estimoinnin luotettavuuden analyysin tekniikoita ja näiden hyödyntämistä edellä mainittujen tehtävien ratkaisuissa. Yleensä ottaen paikallisen liikkeen estimointia vaikeuttaa apertuuriongelma. Tämän vuoksi tarvitaan analyysitekniikoita, jotka kykenevät antamaan täydentävää tietoa liike-estimaattien luotettavuudesta. Työn ensimmäisessä osassa kehitetty analyysimenetelmä käyttää lähtötietona lohkosovituksen kriteerin arvoja, jotka on saatu eri liikekandidaateille. Erotuksena aiempiin menetelmiin kehitetty ratkaisu ottaa huomioon kuvagradientin vaikutuksen. Työn toisessa osassa tutkitaan nelivaiheista piirrepohjaista ratkaisua hallitsevan liikkeen estimoimiseksi. Perushavaintoina mallissa käytetään liikepiirteitä, jotka koostuvat valittujen kuvapisteiden koordinaateista, näissä pisteissä lasketuista liike-estimaateista ja estimaattien epävarmuuden esityksestä. Jälkimmäinen esitetään parametrisessa muodossa käyttäen laskentaan työn ensimmäisessä osassa esitettyä menetelmää. Tätä epävarmuustietoa käytetään piirteiden painottamiseen hallitsevan liikkeen estimoinnissa. Lisäksi tutkitaan gradienttipohjaista piirteiden valintaa. Kokeellisessa osassa erilaisia suunnitteluvalintoja verrataan toisiinsa käyttäen synteettisiä ja todellisia kuvasekvenssejä. Väitöstyön kolmannessa osassa esitetään piirrepohjainen menetelmä taustan ja etualan kohteen liikkeiden erottamiseksi toisistaan. Algoritmi tekee analyysin kahta liikettä sisältävälle näkymälle käyttäen sekä spatiaalista että ajallista segmentointitiedon välittämistä. Piirteiden painotus hyödyntää epävarmuustietoa tässä yhteydessä, jonka osoitetaan kokeellisesti parantavan liike-estimoinnin suorituskykyä. Viimeisessä osassa kehitetään viitekehys liikepohjaisen kohteen ilmaisun, segmentoinnin ja seurannan toteutukselle. Se perustuu lohkopohjaiseen esitystapaan ja näytteistyksen soveltamiseen liikkeen estimoinnissa. Analyysitekniikka segmentoinnin määrittämiseksi esitellään. Lopuksi ratkaisu integroidaan työn kolmannessa osassa esitetyn menetelmän kanssa, ja menetelmien yhdistelmän osoitetaan kokeellisesti parantavan sekä näytteistyksen tehokkuutta että segmentoinnin tarkkuutta

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Staniaszek, Michal. "Feature-Feature Matching For Object Retrieval in Point Clouds." Thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170475.

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In this project, we implement a system for retrieving instances of objects from point clouds using feature based matching techniques. The target dataset of point clouds consists of approximately 80 full scans of office rooms over a period of one month. The raw clouds are reprocessed to remove regions which are unlikely to contain objects. Using locations determined by one of several possible interest point selection methods, one of a number of descriptors is extracted from the processed clouds. Descriptors from a target cloud are compared to those from a query object using a nearest neighbour approach. The nearest neighbours of each descriptor in the query cloud are used to vote for the position of the object in a 3D grid overlaid on the room cloud. We apply clustering in the voting space and rank the clusters according to the number of votes they contain. The centroid of each of the clusters is used to extract a region from the target cloud which, in the ideal case, corresponds to the query object. We perform an experimental evaluation of the system using various parameter settings in order to investigate factors affecting the usability of the system, and the efficacy of the system in retrieving correct objects. In the best case, we retrieve approximately 50% of the matching objects in the dataset. In the worst case, we retrieve only 10%. We find that the best approach is to use a uniform sampling over the room clouds, and to use a descriptor which factors in both colour and shape information to describe points.

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Sim, Hak Chuah. "Invariant object matching with a modified dynamic link network." Thesis, University of Southampton, 1999. https://eprints.soton.ac.uk/256269/.

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Krupnik, Amnon. "Multiple-patch matching in the object space for aerotriangulation /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487857546386844.

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Schellewald, Christian. "Convex Mathematical Programs for Relational Matching of Object Views." [S.l. : s.n.], 2005. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11947807.

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Sjahputera, Ozy. "Object registration in scene matching based on spatial relationships /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144457.

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Smith, David. "Parallel approximate string matching applied to occluded object recognition." PDXScholar, 1987. https://pdxscholar.library.pdx.edu/open_access_etds/3724.

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This thesis develops an algorithm for approximate string matching and applies it to the problem of partially occluded object recognition. The algorithm measures the similarity of differing strings by scanning for matching substrings between strings. The length and number of matching substrings determines the amount of similarity. A classification algorithm is developed using the approximate string matching algorithm for the identification and classification of objects. A previously developed method of shape description is used for object representation.

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Ahmadyfard, Alireza. "Object recognition by region matching using relaxation with relational constraints." Thesis, University of Surrey, 2003. http://epubs.surrey.ac.uk/843289/.

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Our objective in this thesis is to develop a method for establishing an object recognition system based on the matching of image regions. A region is segmented from image based on colour homogeneity of pixels. The method can be applied to a number of computer vision applications such as object recognition (in general) and image retrieval. The motivation for using regions as image primitives is that they can be represented invariantly to a group of geometric transformations and regions are stable under scaling. We model each object of interest in our database using a single frontal image. The recognition task is to determine the presence of object(s) of interest in scene images. We propose a novel method for afflne invariant representation of image regions in the form of Attributed Relational Graph (ARG). To make image regions comparable for matching, we project each region to an affine invariant space and describe it using a set of unary measurements. The distinctiveness of these features is enhanced by describing the relation between the region and its neighbours. We limit ourselves to the low order relations, binary relations, to minimise the combinatorial complexity of both feature extraction and model matching, and to maximise the probability of the features being observed. We propose two sets of binary measurements: geometric relations between pair of regions, and colour profile on the line connecting the centroids of regions. We demonstrate that the former measurements are very discriminative when the shape of segmented regions is informative. However, they are susceptible to distortion of regions boundaries as a result of severe geometric transformations. In contrast, the colour profile binary measurements are very robust. Using this representation we construct a graph to represent the regions in the scene image and refer to it as the scene graph. Similarly a graph containing the regions of all object models is constructed and referred to as the model graph. We consider the object recognition as the problem of matching the scene graph and model graphs. We adopt the probabilistic relaxation labelling technique for our problem. The method is modified to cope better with image segmentation errors. The implemented algorithm is evaluated under affine transformation, occlusion, illumination change and cluttered scene. Good performance for recognition even under severe scaling and in cluttered scenes is reported. Key words: Region Matching, Object Recognition, Relaxation Labelling, Affine Invariant.

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Jeong, Kideog. "OBJECT MATCHING IN DISJOINT CAMERAS USING A COLOR TRANSFER APPROACH." UKnowledge, 2007. http://uknowledge.uky.edu/gradschool_theses/434.

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Object appearance models are a consequence of illumination, viewing direction, camera intrinsics, and other conditions that are specific to a particular camera. As a result, a model acquired in one view is often inappropriate for use in other viewpoints. In this work we treat this appearance model distortion between two non-overlapping cameras as one in which some unknown color transfer function warps a known appearance model from one view to another. We demonstrate how to recover this function in the case where the distortion function is approximated as general affine and object appearance is represented as a mixture of Gaussians. Appearance models are brought into correspondence by searching for a bijection function that best minimizes an entropic metric for model dissimilarity. These correspondences lead to a solution for the transfer function that brings the parameters of the models into alignment in the UV chromaticity plane. Finally, a set of these transfer functions acquired from a collection of object pairs are generalized to a single camera-pair-specific transfer function via robust fitting. We demonstrate the method in the context of a video surveillance network and show that recognition of subjects in disjoint views can be significantly improved using the new color transfer approach.

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Zhang, Jian, and 张简. "Image point matching in multiple-view object reconstruction from imagesequences." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48079856.

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This thesis is concerned with three-dimensional (3D) reconstruction and point registration, which are fundamental topics of numerous applications in the area of computer vision. First, we propose the multiple epipolar lines (MEL) shape recovery method for 3D reconstruction from an image sequence captured under circular motion. This method involves recovering the 3D shape by reconstructing a set of 3D rim curves. The position of each point on a 3D rim curve is estimated by using three or more views. Two or more of these views are chosen close to each other to guarantee good image point matching, while one or more views are chosen far from these views to properly compensate for the error introduced in the triangulation scheme by the short baseline of the close views. Image point matching among all views is performed using a new method that suitably combines epipolar geometry and cross-correlation. Second, we develop the one line search (OLS) method for estimating the 3D model of an object from a sequence of images. The recovered object comprises a set of 3D rim curves. The OLS method determines the image point correspondences of each 3D point through a single line search along the ray defined by the camera center and each two-dimensional (2D) point where a photo-consistency index is maximized. In accordance with the approach, the search area is independently reduced to a line segment on the number of views. The key advantage of the proposed method is that only one variable is focused on in defining the corresponding 3D point, whereas the approaches for multiple-view stereo typically exploit multiple epipolar lines and hence require multiple variables. Third, we propose the expectation conditional maximization for point registration (ECMPR) algorithm to solve the rigid point registration problem by fitting the problem into the framework of maximum likelihood with missing data. The unknown correspondences are handled via mixture models. We derive a maximization criterion based on the expected complete-data log-likelihood. Then, the point registration problem can be solved by an instance of the expectation conditional maximization algorithm, that is, the ECMPR algorithm. Experiments with synthetic and real data are presented in each section. The proposed approaches provide satisfactory and promising results. published_or_final_version Electrical and Electronic Engineering Doctoral Doctor of Philosophy

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Neal, Pamela J. "Finding and matching topographic features in 3-D object meshes /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/5949.

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Steliaros, Michael Konstantinos. "Motion compensation for 2D object-based video coding." Thesis, University of Warwick, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340917.

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Geller, Felix, Robert Hirschfeld, and Gilad Bracha. "Pattern Matching for an object-oriented and dynamically typed programming language." Universität Potsdam, 2010. http://opus.kobv.de/ubp/volltexte/2010/4303/.

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Pattern matching is a well-established concept in the functional programming community. It provides the means for concisely identifying and destructuring values of interest. This enables a clean separation of data structures and respective functionality, as well as dispatching functionality based on more than a single value. Unfortunately, expressive pattern matching facilities are seldomly incorporated in present object-oriented programming languages. We present a seamless integration of pattern matching facilities in an object-oriented and dynamically typed programming language: Newspeak. We describe language extensions to improve the practicability and integrate our additions with the existing programming environment for Newspeak. This report is based on the first author’s master’s thesis.

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Jones, Michael J. (Michael Jeffrey) 1968. "Multidimensional morphable models : a framework for representing and matching object classes." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43399.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997. Includes bibliographical references (p. 129-133). by Michel Jeffrey Jones. Ph.D.

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Lind, Anders. "High-speed View Matching using Region Descriptors." Thesis, Linköping University, Computer Vision, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-58843.

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This thesis treats topics within the area of object recognition. A real-time view matching method has been developed to compute the transformation between two different images of the same scene. This method uses a color based region detector called MSCR and affine transformations of these regions to create affine-invariant patches that are used as input to the SIFT algorithm. A parallel method to compute the SIFT descriptor has been created with relaxed constraints so that the descriptor size and the number of histogram bins can be adjusted. Additionally, a matching step to deduce correspondences and a parallel RANSAC method have been created to estimate the undergone transformation between these descriptors. To achieve real-time performance, the implementation has been targeted to use the parallel nature of the GPU with CUDA as the programming language. Focus has been put on the architecture of the GPU to find the best way to parallelize the different processing steps. CUDA has also been combined with OpenGL to be able to use the hardware accelerated anisotropic sampling method for affine transformations of regions. Parts of the implementation can also be used individually from either Matlab or by using the provided C++ library directly. The method was also evaluated in terms of accuracy and speed. It was shown that our algorithm has similar or better accuracy at finding correspondences than SIFT when the 3D geometry changes are large but we get a slightly worse result on images with flat surfaces.

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Rubio, Ballester Jose C. "Many-to-Many High Order Matching. Applications to Tracking and Object Segmentation." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/96481.

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La correspondència de característiques és un problema fonamental de la Visió per Computador, que té múltiples aplicacions com el seguiment, la classificació i recuperació d’imatges, el reconeixement de formes i la visió estereoscòpica. En molts àmbits, és útil per representar l’estructura local de les carácterístiques en correspondència, per augmentar la precissió o per fer les correspondències invariants a certes transformacions (afins, homografies, etc...). No obstant això, la codificació d’aquest coneixement requereix complicar el model mitjançant l’establiment de relacions d’ordre alt entre els elements del model, i per tant l’augment de la complexitat del problema d’optimització. La importància de les correspondències molts-a-molts es de vegades ignorada en la literatura. La majoria dels mètodes es limiten a realizar correspondències un-a-un, generalment validant en conjunts de dades sintètiques, o no realistes. En un entorn real, amb variacions d’escala, il.luminació i orientació de l’objecte d’interés, i amb la presència d’oclusions, desordre, i observacions sorolloses, les relacions molts-a-molts son necessàries per aconseguir resultats satisfactoris. Com a conseqüència, trovar la correspondència molts-a-molts més probable, implica un procés complicat d’optimització combinatòria. En aquest treball dissenyem i demostrem algorismes de correspondència que calculen associacions molts-a-molts, i que poden ser aplicats a diversos problemes difícils de resoldre. El nostre objectiu és fer ús de representacios d’ordre alt per millorar el poder expressiu de la correspondència, alhora que ferm possible el procés d’inferència o l’optimització d’aquests models. Al llarg de la tesi, hem utilitzat eficaçment els models gràfics com la nostra representació preferida, ja que proporcionen un marc probabilístic elegant per abordar problemes de predicció estructurada. Hem introdüit un algorisme de seguiment bassat en correspondències que es porten a terme entre els fotogrames d’una sequència de vídeo, per tal de resoldre el problema de segument de fars de cotxes durant la nit. També generalitzem aquest mateix algorisme per resoldre el problema de l’associació de dades aplicat a different escenaris de seguiment. Hem demostrat l’eficàcia d’aquest enfoc en seqüències de vídeo reals i demostrem que el nostre algorisme de seguiment es pot utilitzar per millorar la precisió d’un sistema de classificació de fars de cotxes. A la segona part d’aquest treball, pasem desde correspondències no denses (punts) cap a correspondèencies denses (regions), i introdüim una nova representació jeràrquica d’imatges. Seguidament, fem ús d’aquest model per desenvolupar correspondències molts-a-molts d’ordre alt entre parelles d’imatges. Demostrem que l’ús de models d’ordre alt en comparació amb altres models més senzills no només millora l’exactitud dels resultats, sinó també la velocitat de convergència de l’algorisme d’inferència. Finalment, seguim explotant la idea de correspondència de regions per dissenyar un algorisme de co-segmentació completament no supervisat, que és capaç de competir amb altres mètodes supervisats de l’estat-de-l’art. El nostre mètode supera inconvenients típics d’alguns treballs passats, com evitar la necesitat d’aparences variades al fons de les imatges. La correspondència de regions en aquest cas s’aplica per explotar eficaçment la informació compartida entre les imatges. També extenem aquest treball per dur a terme co-segmentació de vídeos, sent la primera vegada que s’aborda aquest problema. Feature matching is a fundamental problem in Computer Vision, having multiple applications such as tracking, image classification and retrieval, shape recognition and stereo fusion. In numerous domains, it is useful to represent the local structure of the matching features to increase the matching accuracy or to make the correspondence invariant to certain transformations (affine, homography, etc…). However, ncoding this knowledge requires complicating the model by establishing high-order relationships between the model elements, and therefore increasing the complexity of the optimization problem. The importance of many-to-many matching is sometimes dismissed in the literature. Most methods are restricted to perform one-to-one matching, and are usually validated on synthetic, or non-realistic datasets. In a real challenging environment, with scale, pose and illumination variations of the object of interest, as well as the presence of occlusions, clutter, and noisy observations, many-to-many matching is necessary to achieve satisfactory results. As a consequence, finding the most likely many-to-many correspondence often involves a challenging combinatorial optimization process. In this work, we design and demonstrate matching algorithms that compute many-to-many correspondences, applied to several challenging problems. Our goal is to make use of high-order representations to improve the expressive power of the matching, at the same time that we make feasible the process of inference or optimization of such models. We effectively use graphical models as our preferred representation because they provide an elegant probabilistic framework to tackle structured prediction problems. We introduce a matching-based tracking algorithm which performs matching between frames of a video sequence in order to solve the difficult problem of headlight tracking at night-time. We also generalize this algorithm to solve the problem of data association applied to various tracking scenarios. We demonstrate the effectiveness of such approach in real video sequences and we show that our tracking algorithm can be used to improve the accuracy of a headlight classification system. In the second part of this work, we move from single (point) matching to dense (region) matching and we introduce a new hierarchical image representation. We make use of such model to develop a high-order many-to-many matching between pairs of images. We show that the use of high-order models in comparison to simpler models improves not only the accuracy of the results, but also the convergence speed of the inference algorithm. Finally, we keep exploiting the idea of region matching to design a fully unsupervised image cosegmentation algorithm that is able to perform competitively with state-of-the-art supervised methods. Our method also overcomes the typical drawbacks of some of the past works, such as avoiding the necessity of variate appearances on the image backgrounds. The region matching in this case is applied to effectively exploit inter-image information. We also extend this work to perform co-segmentation of videos, being the first time that such problem is addressed, as a way to perform video object segmentation.

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Boros, Peter. "Object Recognition: Modelling and the Interface to a Control Strategy for Matching." Doctoral thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1690.

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Amodelling system for object recognition and pose estimation is presented in this work, based on approximating the aspect/appearance graph of arbitrary rigid objects for a spherical viewing surface using simulated image data. The approximation is achieved by adaptively subdividing the viewing sphere starting with an icosahedral tessellation and iteratively decreasing the patch size until the desired resolution is reached. The adaptive subdivision is controlled by both the required resolution and object detail. The decision whether a patch should be divided is based on a similarity measure, which is obtained from applying graph matching to attributed relational graphs generated from image features.Patches surrounded by similar views are grouped together and reference classes for the aspects are established. The reference classes are indexed by contour types encountered in the views within the group, where the contour types are computed via unsupervised clustering performed on the complete set of contours for all views of a given object.Classification of an unknown pose is done efficiently via simple or weighted bipartite matching of the contours extracted from the unknown pose to the equivalence classes. The best suggestions are selected by a scoring scheme applied to the match results.Themodelling system is demonstrated by experimental results for a number of objects at varying levels of resolution. Pose estimation results from both synthetic and real images are also presented.

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Mian, Ajmal Saeed. "Representations and matching techniques for 3D free-form object and face recognition." University of Western Australia. School of Computer Science and Software Engineering, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0046.

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[Truncated abstract] The aim of visual recognition is to identify objects in a scene and estimate their pose. Object recognition from 2D images is sensitive to illumination, pose, clutter and occlusions. Object recognition from range data on the other hand does not suffer from these limitations. An important paradigm of recognition is model-based whereby 3D models of objects are constructed offline and saved in a database, using a suitable representation. During online recognition, a similar representation of a scene is matched with the database for recognizing objects present in the scene . . . The tensor representation is extended to automatic and pose invariant 3D face recognition. As the face is a non-rigid object, expressions can significantly change its 3D shape. Therefore, the last part of this thesis investigates representations and matching techniques for automatic 3D face recognition which are robust to facial expressions. A number of novelties are proposed in this area along with their extensive experimental validation using the largest available 3D face database. These novelties include a region-based matching algorithm for 3D face recognition, a 2D and 3D multimodal hybrid face recognition algorithm, fully automatic 3D nose ridge detection, fully automatic normalization of 3D and 2D faces, a low cost rejection classifier based on a novel Spherical Face Representation, and finally, automatic segmentation of the expression insensitive regions of a face.

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Ta, Anh Phuong. "Inexact graph matching techniques : application to object detection and human action recognition." Lyon, INSA, 2010. http://theses.insa-lyon.fr/publication/2010ISAL0099/these.pdf.

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Object detection and human action recognition are two active fields of research in computer vision, which have applications ranging from robotics and video surveillance, medical image analysis, human-computer interactions to content-based video annotation and retrieval. At this time, building such robust recognition systems still remain very challenging tasks, because of the variations in action/object classes, different possible viewpoints, as well as illumination changes, moving cameras, complex dynamic backgrounds and occlusions. In this thesis, we deal with object and activity recognition problems. Despite differences in the applications’ goals, the associated fundamental problems share numerous properties, for instance the necessity of handling non-rigid transformations. Describing a model object or a video by a set of local features, we formulate the recognition problem as a graph matching problem, where nodes represent local features, and edges represent spatial and/or spatio-temporal relationships between them. Inexact matching of valued graphs is a well known NP-hard problem, therefore we concentrated on finding approximate solutions. To this end, the graph matching problem is formulated as an energy minimization problem. Based on this energy function, we propose two different solutions for the two applications: object detection in images and activity recognition in video sequences. We also propose new features to improve the conventional Bag of words model, which is widely used in computer vision. Experiments on both standard datasets and our own datasets, demonstrate that our methods provide good results regarding the recent state-of-the-art in both domains La détection d’objets et la reconnaissance des activités humaines sont les deux domaines actifs dans la vision par ordinateur, qui trouve des applications en robotique, vidéo surveillance, analyse des images médicales, interaction homme-machine, annotation et recherche de la vidéo par le contenue. Actuellement, il reste encore très difficile de construire de tels systèmes, en raison des variations des classes d’objets et d’actions, les différents points de vue, ainsi que des changements d’illumination, des mouvements de caméra, des fonds dynamiques et des occlusions. Dans cette thèse, nous traitons le problème de la détection d’objet et d’activités dans la vidéo. Malgré ses différences de buts, les problèmes fondamentaux associés partagent de nombreuses propriétés, par exemple la nécessité de manipuler des transformations non-ridiges. En décrivant un modèle d’objet ou une vidéo par un ensemble des caractéristiques locales, nous formulons le problème de reconnaissance comme celui d’une mise en correspondance de graphes, dont les nœuds représentent les caractéristiques locales, et les arrêtes représentent les relations que l’on veut vérifier entre ces caractéristiques. Le problème de mise en correspondance inexacte de graphes est connu comme NP-difficile, nous avons donc porté notre effort sur des solutions approchées. Pour cela, le problème est transformé en problème d’optimisation d’une fonction d’énergie, qui contient un terme en rapport avec la distance entre les descripteurs locaux et d’autres termes en rapport avec les relations spatiales (ou/et temporelles) entre eux. Basé sur cette énergie, deux différentes solutions ont été proposées et validées pour les deux applications ciblées: la reconnaissance d’objets à partir d’images et la reconnaissance des activités dans la vidéo. En plus, nous avons également proposé un nouveaux descripteur pour améliorer les modèles de Sac-de-mots, qui sont largement utilisé dans la vision par ordinateur. Nos expérimentations sur deux bases standards, ainsi que sur nos bases démontrent que les méthodes proposées donnent de bons résultats en comparant avec l’état de l’art dans ces deux domaines

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Mian, Ajmal Saeed. "Representations and matching techniques for 3D free-form object and face recognition /." Connect to this title, 2006. http://theses.library.uwa.edu.au/adt-WU2007.0046.

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Planitz, Brigit Maria. "A correspondence framework for surface matching algorithms." Queensland University of Technology, 2004. http://eprints.qut.edu.au/16001/.

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Computer vision tasks such as three dimensional (3D) registration, 3D modelling, and 3D object recognition are becoming more and more useful in industry, and have application such as reverse CAD engineering, and robot navigation. Each of these applications use correspondence algorithms as part of their processes. Correspondence algorithms are required to compute accurate mappings between artificial surfaces that represent actual objects or scenes. In industry, inaccurate correspondence is related to factors such as expenses in time and labour, and also safety. Therefore, it is essential to select an appropriate correspondence algorithm for a given surface matching task. However, current research in the area of surface correspondence is hampered by an abundance of applications specific algorithms, and no uniform terminology of consistent model for selecting and/or comparing algorithms. This dissertation presents a correspondence framework for surface matching algorithms. The framework is a conceptual model that is implementable. It is designed to assist in the analysis, comparison, development, and implementation of correspondence algorithms, which are essential tasks when selecting or creating an algorithm for a particular application. The primary contribution of the thesis is the correspondence framework presented as a conceptual model for surface matching algorithms. The model provides a systematic method for analysing, comparing, and developing algorithms. The dissertation demonstrates that by dividing correspondence computation into five stages: region definition, feature extraction, feature representation, local matching, and global matching, the task becomes smaller and more manageable. It also shows that the same stages of different algorithms are directly comparable. Furthermore, novel algorithms can be created by simply connecting compatible stages of different algorithms. Finally, new ideas can be synthesised by creating only the stages to be tested, without developing a while new correspondence algorithm. The secondary contribution that is outlined is the correspondence framework presented as a software design tool for surface matching algorithms. The framework is shown to reduce the complexity of implementing existing algorithms within the framework. This is done by encoding algorithms in a stage-wise procedure, whereby an algorithm is separated into the five stages of the framework. The software design tool is shown to validate the integrity of restructuring existing algorithms within it, and also provide an efficient basis for creating new algorithms. The third contribution that is made is the specification of a quality metric for algorithms comparison. The metric is used to assess the accuracy of the outcomes of a number of correspondence algorithms, which are used to match a wide variety of input surface pairs. The metric is used to demonstrate that each algorithm is application specific, and highlight the types of surfaces that can be matched by each algorithm. Thus, it is shown that algorithms that are implemented within the framework can be selected for particular surface correspondence tasks. The final contribution made is this dissertation is the expansion of the correspondence framework beyond the surface matching domain. The correspondence framework is maintained in its original form, and is used for image matching algorithms. Existing algorithms from three image matching applications are implemented and modified using the framework. It is shown how the framework provides a consistent means and uniform terminology for developing both surface and image matching algorithms. In summary, this thesis presents a correspondence framework for surface matching algorithms. The framework is general, encompassing a comprehensive set of algorithms, and flexible, expanding beyond surface matching to major image matching applications.

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Planitz, Birgit. "A correspondence framework for surface matching algorithms." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/16001/1/Brigit_Planitz_Thesis.pdf.

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Breuel, Thomas M. "Geometric Aspects of Visual Object Recognition." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/7342.

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This thesis presents there important results in visual object recognition based on shape. (1) A new algorithm (RAST; Recognition by Adaptive Sudivisions of Tranformation space) is presented that has lower average-case complexity than any known recognition algorithm. (2) It is shown, both theoretically and empirically, that representing 3D objects as collections of 2D views (the "View-Based Approximation") is feasible and affects the reliability of 3D recognition systems no more than other commonly made approximations. (3) The problem of recognition in cluttered scenes is considered from a Bayesian perspective; the commonly-used "bounded-error errorsmeasure" is demonstrated to correspond to an independence assumption. It is shown that by modeling the statistical properties of real-scenes better, objects can be recognized more reliably.

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Thakkar, Chintan. "Ventricle slice detection in MRI images using Hough Transform and Object Matching techniques." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001815.

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Tewes, Andreas H. [Verfasser]. "A Flexible Object Model for Encoding and Matching Human Faces / Andreas H Tewes." Aachen : Shaker, 2006. http://d-nb.info/1170529097/34.

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Schrider, Christina Da-Wann. "Histogram-based template matching object detection in images with varying brightness and contrast." Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1224044521.

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Thompson, Emma. "Similar but Different: How Foraging Bumblebees ('Bombus Impatiens') Treat Flowers and Pictures of Flowers." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35032.

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Flowers, the sole natural source of pollen and nectar for bees, present many similar features, in colour, shape, size and scent, which facilitate pollinator attraction. This similarity among stimuli requires perception of commonality but also a capacity for differentiation between similar but different stimuli. While many flowers of a similar type will elicit approach and foraging, failure to access resources on any individual flower in an array (e.g. due to depletion) will not necessarily generalize and deter further foraging. Such conditions demand that bees respond to both the similarity and differences among stimuli which may share many common features but differ individually in available resources. Two questions are raised by this challenge and will herein be addressed: how do bees perceive and respond to ‘similar but different’ stimuli? And, how do bees use such cues to find rewarding flowers? Picture-object correspondence has not been previously specifically studied in invertebrates. The correspondence between picture-cue and object stimuli may offer a unique opportunity to trigger memory for corresponding targets while still retaining an important distinction between unrewarding cue and rewarding targets. Perception of pictures is not always perceived by animals as either the same as or equivalent to the intended subject. According to Fagot et al. (2000) the perceived relationship may result in confusion, independence or equivalence and is dependent upon experience. The objectives of this thesis are twofold: first, determine how bumblebees (Bombus impatiens) perceive the relationship between objects and corresponding pictures and secondly, to determine whether or not bees may be able to attend to and use pictures as cues while foraging. The correspondence of picture and object by bees was evaluated with four experiments of preference: (1) learned differentiation; spontaneous association to (2) colour, and (3) achromatic, impoverished images; and (4) learned picture cue use. Firstly, results show that bees do not confuse an object with a corresponding picture but nevertheless do perceive a relationship between them if colour cues are retained. Altered, achromatic images were not consistently treated as corresponding to coloured objects. Secondly, bees can learn to use a picture cue in a delayed matching foraging task. Results further suggest a role of three contributing factors in bumblebee picture cue use: (i) conditions of high inconsistency as to which target will be rewarding; (ii) stable target locations; and (iii) individual foraging experience. It appears that bumblebees can learn to use cues, in a delayed matching task, when the location of the corresponding target is known and stable, the individual bee has acquired some experience in successful foraging, and reward is otherwise unpredictable without the use of the cue. Bees may disregard secondary cues as noise under conditions of high target predictability whereby floral constancy or target perseveration may be most efficient, but attend to and learn such cues as signals if target reward is highly unpredictable. The conditions for this sensitivity may coincide with naturally occurring floral cycles.

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Skalski, Tomasz, and Witold Zaborowski. "Object detection and pose estimation of randomly organized objects for a robotic bin picking system." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2153.

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Today modern industry systems are almost fully automated. The high requirements regarding speed, flexibility, precision and reliability makes it in some cases very difficult to create. One of the most willingly researched solution to solve many processes without human influence is bin-picking. Bin picking is a very complex process which integrates devices such as: robotic grasping arm, vision system, collision avoidance algorithms and many others. This paper describes the creation of a vision system - the most important part of the whole bin-picking system. Authors propose a model-based solution for estimating a best pick-up candidate position and orientation. In this method database is created from 3D CAD model, compared with processed image from the 3D scanner. Paper widely describes database creation from 3D STL model, Sick IVP 3D scanner configuration and creation of the comparing algorithm based on autocorrelation function and morphological operators. The results shows that proposed solution is universal, time efficient, robust and gives opportunities for further work. +4915782529118

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Nigri, Simone. "Ottimizzatore per configurazione automatica di algoritmi di pattern matching." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Il tema del riconoscimento di oggetti, siano essi microscopici componenti in grandi catene di assemblaggio o segnali grafici posti su pacchi in spedizione, è uno degli argomenti più rilevanti all'interno del vasto mondo racchiuso nel settore interdisciplinare rappresentato da Computer Vision, inerente all'analisi di immagini al fine di estrapolarne informazioni. Nonostante ciò, l'utilizzo e la configurazione di strumenti (o tool) il cui obiettivo è esattamente quello di rilevare particolari modelli noti a priori in diverse immagini, potrebbe essere un compito particolarmente arduo per un utente medio senza preventiva conoscenza in tale ambito. Al fine di aiutare e guidare tale settore di utenza, lo scopo dell'elaborato sviluppato è, quindi, quello di creare un sistema di ottimizzazione automatica del processo di configurazione di strumenti dediti al Pattern Matching, considerando come study-case il setup del componente software Pattern Sort gentilmente messo a disposizione da Datalogic S.p.A.

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Monroy, Kuhn Juan Antonio [Verfasser], and Björn [Akademischer Betreuer] Ommer. "Morphological Analysis for Object Recognition, Matching, and Applications / Juan Antonio Monroy Kuhn ; Betreuer: Björn Ommer." Heidelberg : Universitätsbibliothek Heidelberg, 2013. http://d-nb.info/1177809354/34.

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Monroy, Juan Antonio [Verfasser], and Björn [Akademischer Betreuer] Ommer. "Morphological Analysis for Object Recognition, Matching, and Applications / Juan Antonio Monroy Kuhn ; Betreuer: Björn Ommer." Heidelberg : Universitätsbibliothek Heidelberg, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:16-heidok-156637.

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Cui, Beibei. "Image processing applications in object detection and graph matching : from Matlab development to GPU framework." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA002.

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Déterminer des mises en correspondance d’objet, ou de caractéristiques d’objet, dans des images présente un grand intérêt pour beaucoup d’applications telles que la détection et le suivi de cible, l’estimation du flot optique, l’identification, et d’autres tâches dérivées. Dans cette thèse, nous abordons le problème de mise en correspondance dans le cadre général de l’optimisation de l’appariement de graphe, dans le but de contribuer, comme résultat final, au développement de nouveaux algorithmes parallèles implémentés sur plateforme GPU (Graphics Processing Unit). Le problème d’appariement de graphe peut être décliné de diverses manières suivant l’application considérée. Nous observons un fossé entre les applications basées sur des fonctions de coût locales et les applications avec des fonctions de coût d’ordre supérieur, évaluant la similarité entre les arêtes du graphe, ou les hyperliens lorsqu’il s’agit d’un hypergraphe. La première classe d’applications comporte des algorithmes de résolution basés sur des calculs de convolution et possède déjà des implémentations parallèles sur GPU. La deuxième classe d’applications met l’accent sur les relations géométriques entre caractéristiques extraites de l’image, transformant le problème de mise en correspondance en un programme quadratique en nombre entiers avec contraintes, pour lequel nous n’avons pas trouvé de solution GPU accessible actuellement.Deux types d’approche ont été adoptées pour contribuer à la problématique d’appariement de graphe sur GPU. Premièrement, nous étudions différentes déclinaisons de cette problématique via l’utilisation de la plateforme Matlab afin de pouvoir réutiliser et fournir des solutions représentatives de l’état de l’art, ainsi que des protocoles d’expérimentation et des données d’entrée nécessaires pour une plateforme GPU dédiée à l’évaluation et la comparaison avec les algorithmes séquentiels sur Matlab. Ainsi, une première partie du travail concerne trois contributions respectivement, aux techniques de soustraction d’arrière-plan et de différence d’image pour la détection, au problème d’extraction de caractéristiques pour la mise en correspondance, et au problème général d’appariement de graphe, toutes basées sur la combinaison de méthodes issues de l’environnement Matlab. Deuxièmement, nous proposons une infrastructure logicielle GPU nouvelle, écrite en CUDA C++, spécifiquement adaptée au problème d’appariement de graphe géométrique, proposant de nouveaux algorithmes parallèles de complexité calculatoire plus réduite, tels que les cartes auto-organisatrices dans le plan, des algorithmes de cluster qui en sont dérivés, et des recherches locales distribuées. Ces algorithmes parallèles sont évalués et comparés aux approches de l’état de l’art pour le problème d’appariement de graphe, en suivant un protocole d’expérimentation identique. Cette plateforme GPU constitue notre principale proposition pour contribuer à combler le fossé entre développement GPU et son application au problème général d’appariement de graphe Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, flow velocity estimation, identification, registration, and many derived tasks. In this thesis, we address feature correspondence within the general framework of graph matching optimization and with the principal aim to contribute, at a final step, to the design of new and parallel algorithms and their implementation on GPU (Graphics Processing Unit) systems. Graph matching problems can have many declinations, depending on the assumptions of the application at hand. We observed a gap between applications based on local cost objective functions, and those applications with higher-order cost functions, that evaluate similarity between edges of the graphs, or hyperedges when considering hypergraphs. The former class provides convolution-based algorithms already having parallel GPU implementations. Whereas, the latter class puts the emphasis on geometric inter-feature relationships, transforming the correspondence problem to a purely geometric problem stated in a high dimensional space, generally modeled as an integer quadratic programming, for which we did not find GPU implementations available yet.Two complementary approaches were adopted in order to contribute to addressing higher-order geometric graph matching on GPU. Firstly, we study different declinations of feature correspondence problems by the use of the Matlab platform, in order to reuse and provide state-of-the-art solution methods, as well as experimental protocols and input data necessary for a GPU platform with evaluation and comparison tools against existing sequential algorithms, most of the time developed in Matlab framework. Then, the first part of this work concerns three contributions, respectively, to background and frame difference application, to feature extraction problem from images for local correspondences, and to the general graph matching problem, all based on the combination of methods derived from Matlab environment. Secondly, and based on the results of Matlab developments, we propose a new GPU framework written in CUDA C++ specifically dedicated to geometric graph matching but providing new parallel algorithms, with lower computational complexity, as the self-organizing map in the plane, derived parallel clustering algorithms, and distributed local search method. These parallel algorithms are then evaluated and compared to the state-of-the-art methods available for graph matching and following the same experimental protocol. This GPU platform constitutes our final and main proposal to contribute to bridging the gap between GPU development and higher-order graph matching

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Mora, Leonardo. "Un algoritmo di pattern matching basato sull'estrazione di segmenti rettilinei." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Il ritrovamento di oggetti all'interno di immagini rappresenta un parte importante del settore di competenza della Visione Artificiale. Questa importanza è dovuta al fatto che molto sistemi industriali per essere autonomi, necessitano di informazioni relative allo spazio circostante e ai vari oggetti presenti. L'obiettivo di questa tesi è stato quello di sviluppare un algoritmo di "pattern matching" basato su segmenti rettilinei. Nel primo capitolo si è fatta una panoramica sulle varie strategie utilizzate dagli algoritmi di object detection.\\ Il secondo illustra l'estrattore di linee utilizzato nell'algoritmo. Nel terzo capitolo vengono descritte le soluzioni realizzate, mentre nel quarto sono raccolti alcuni dei risultati ottenuti.

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Pettersson, Johan. "Real-time Object Recognition on a GPU." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10238.

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Shape-Based matching (SBM) is a known method for 2D object recognition that is rather robust against illumination variations, noise, clutter and partial occlusion.The objects to be recognized can be translated, rotated and scaled.The translation of an object is determined by evaluating a similarity measure for all possible positions (similar to cross correlation).The similarity measure is based on dot products between normalized gradient directions in edges.Rotation and scale is determined by evaluating all possible combinations, spanning a huge search space.A resolution pyramid is used to form a heuristic for the search that then gains real-time performance.For SBM, a model consisting of normalized edge gradient directions, are constructed for all possible combinations of rotation and scale.We have avoided this by using (bilinear) interpolation in the search gradient map, which greatly reduces the amount of storage required.SBM is highly parallelizable by nature and with our suggested improvements it becomes much suited for running on a GPU.This have been implemented and tested, and the results clearly outperform those of our reference CPU implementation (with magnitudes of hundreds).It is also very scalable and easily benefits from future devices without effort.An extensive evaluation material and tools for evaluating object recognition algorithms have been developed and the implementation is evaluated and compared to two commercial 2D object recognition solutions.The results show that the method is very powerful when dealing with the distortions listed above and competes well with its opponents.

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Ilestrand, Maja. "Automatic Eartag Recognition on Dairy Cows in Real Barn Environment." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139245.

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All dairy cows in Europe wear unique identification tags in their ears. These eartags are standardized and contains the cows identification numbers, today only used for visual identification by the farmer. The cow also needs to be identified by an automatic identification system connected to milk machines and other robotics used at the farm. Currently this is solved with a non-standardized radio transmitter which can be placed on different places on the cow and different receivers needs to be used on different farms. Other drawbacks with the currently used identification system are that it is expensive and unreliable. This thesis explores the possibility to replace this non standardized radio frequency based identification system with a standardized computer vision based system. The method proposed in this thesis uses a color threshold approach for detection, a flood fill approach followed by Hough transform and a projection method for segmentation and evaluates template matching, k-nearest neighbour and support vector machines as optical character recognition methods. The result from the thesis shows that the quality of the data used as input to the system is vital. By using good data, k-nearest neighbour, which showed the best results of the three OCR approaches, handles 98 % of the digits.

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Thanikasalam, Kokul. "Appearance based online visual object tracking." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/130875/1/Kokul_Thanikasalam_Thesis.pdf.

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This thesis presents research contributions to the field of computer vision based visual object tracking. This study investigates appearance based object tracking by using traditional hand-crafted and deep features. The thesis proposes a real-time tracking framework with high accuracy which follows a deep similarity tracking strategy. This thesis also proposes several deep tracking frameworks for high-accuracy tracking and to manage the spatial information loss. The research findings of the study would be able to be used in a range of applications including visual surveillance systems.

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Duchenne, Olivier. "Non-rigid image alignment for object recognition." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00906074.

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La vision permet aux animaux de recueillir une information riche et détaillée sur leur environnent proche ou lointain. Les machines ont aussi accès à cette information riche via leurs caméras. Mais, elles n'ont pas encore le logiciel adéquat leur permettant de la traiter pour transformer les valeurs brutes des pixels de l'image en information plus utile telle que la nature, la position, et la fonction des objets environnants. Voilà une des raisons pour laquelle il leur est difficile de se mouvoir dans un environnement inconnu, et d'interagir avec les humains ou du matériel dans des scénarios non-planifiés. Cependant, la conception de ce logiciel comporte de multiples défis. Parmi ceux-ci, il est difficile de comparer deux images entre elles, par exemple, afin que la machine puisse reconnaître que ce qu'elle voit est similaire à une image qu'elle a déjà vue et identifiée. Une des raisons de cette difficulté est que la machine ne sait pas, a priori, quelles parties des deux images se correspondent, et ne sait donc pas quoi comparer avec quoi. Cette thèse s'attaque à ce problème et propose une série d'algorithmes permettant de trouver les parties correspondantes entre plusieurs images, ou en d'autre terme d'aligner les images. La première méthode proposée permet d'apparier ces parties de manière cohérente en prenant en compte les interactions entre plus de deux d'entre elles. Le deuxième algorithme proposé applique avec succès une méthode d'alignement pour déterminer la catégorie d'un objet centré dans une image. Le troisième est optimisé pour la vitesse et tente de détecter un objet d'une catégorie donné où qu'il soit dans l'image.

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Aleixo, Patrícia Nunes. "Object detection and recognition for robotic applications." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13811.

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Mestrado em Engenharia Eletrónica e Telecomunicações The computer vision assumes an important relevance in the development of robotic applications. In several applications, robots need to use vision to detect objects, a challenging and sometimes difficult task. This thesis is focused on the study and development of algorithms to be used in detection and identification of objects on digital images to be applied on robots that will be used in practice cases. Three problems are addressed: Detection and identification of decorative stones for textile industry; Detection of the ball in robotic soccer; Detection of objects in a service robot, that operates in a domestic environment. In each case, different methods are studied and applied, such as, Template Matching, Hough transform and visual descriptors (like SIFT and SURF). It was chosen the OpenCv library in order to use the data structures to image manipulation, as well as other structures for all information generated by the developed vision systems. Whenever possible, it was used the implementation of the described methods and have been developed new approaches, both in terms of pre-processing algorithms and in terms of modification of the source code in some used functions. Regarding the pre-processing algorithms, were used the Canny edge detector, contours detection, extraction of color information, among others. For the three problems, there are presented and discussed experimental results in order to evaluate the best method to apply in each case. The best method for each application is already integrated or in the process of integration in the described robots. A visão por computador assume uma importante relevância no desenvolvimento de aplicações robóticas, na medida em que há robôs que precisam de usar a visão para detetar objetos, uma tarefa desafiadora e por vezes difícil. Esta dissertação foca-se no estudo e desenvolvimento de algoritmos para a deteção e identificação de objetos em imagem digital para aplicar em robôs que serão usados em casos práticos. São abordados três problemas: Deteção e identificação de pedras decorativas para a indústria têxtil; Deteção da bola em futebol robótico; Deteção de objetos num robô de serviço, que opera em ambiente doméstico. Para cada caso, diferentes métodos são estudados e aplicados, tais como, Template Matching, transformada de Hough e descritores visuais (como SIFT e SURF). Optou-se pela biblioteca OpenCv com vista a utilizar as suas estruturas de dados para manipulação de imagem, bem como as demais estruturas para toda a informação gerada pelos sistemas de visão desenvolvidos. Sempre que possivel utilizaram-se as implementações dos métodos descritos tendo sido desenvolvidas novas abordagens, quer em termos de algoritmos de preprocessamento quer em termos de alteração do código fonte das funções utilizadas. Como algoritmos de pre-processamento foram utilizados o detetor de arestas Canny, deteção de contornos, extração de informação de cor, entre outros. Para os três problemas, são apresentados e discutidos resultados experimentais, de forma a avaliar o melhor método a aplicar em cada caso. O melhor método em cada aplicação encontra-se já integrado ou em fase de integração dos robôs descritos.

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Li, Bo. "Interest Curves : Concept, Evaluation, Implementation and Applications." Doctoral thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-111175.

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Image features play important roles in a wide range of computer vision applications, such as image registration, 3D reconstruction, object detection and video understanding. These image features include edges, contours, corners, regions, lines, curves, interest points, etc. However, the research is fragmented in these areas, especially when it comes to line and curve detection. In this thesis, we aim to discover, integrate, evaluate and summarize past research as well as our contributions in the area of image features. This thesis provides a comprehensive framework of concept, evaluation, implementation, and applications for image features. Firstly, this thesis proposes a novel concept of interest curves. Interest curves is a concept derived and extended from interest points. Interest curves are significant lines and arcs in an image that are repeatable under various image transformations. Interest curves bring clear guidelines and structures for future curve and line detection algorithms and related applications. Secondly, this thesis presents an evaluation framework for detecting and describing interest curves. The evaluation framework provides a new paradigm for comparing the performance of state-of-the-art line and curve detectors under image perturbations and transformations. Thirdly, this thesis proposes an interest curve detector (Distinctive Curves, DICU), which unifies the detection of edges, corners, lines and curves. DICU represents our state-of-the-art contribution in the areas concerning the detection of edges, corners, curves and lines. Our research efforts cover the most important attributes required by these features with respect to robustness and efficiency. Interest curves preserve richer geometric information than interest points. This advantage gives new ways of solving computer vision problems. We propose a simple description method for curve matching applications. We have found that our proposed interest curve descriptor outperforms all state-of-the-art interest point descriptors (SIFT, SURF, BRISK, ORB, FREAK). Furthermore, in our research we design a novel object detection algorithm that only utilizes DICU geometries without using local feature appearance. We organize image objects as curve chains and to detect an object, we search this curve chain in the target image using dynamic programming. The curve chain matching is scale and rotation-invariant as well as robust to image deformations. These properties have given us the possibility of resolving the rotation-variance problem in object detection applications. In our face detection experiments, the curve chain matching method proves to be scale and rotation-invariant and very computational efficient. Bilddetaljer har en viktig roll i ett stort antal applikationer för datorseende, t.ex., bildregistrering, 3D-rekonstruktion, objektdetektering och videoförståelse. Dessa bilddetaljer inkluderar kanter, konturer, hörn, regioner, linjer, kurvor, intressepunkter, etc. Forskningen inom dessa områden är splittrad, särskilt för detektering av linjer och kurvor. I denna avhandling, strävar vi efter att hitta, integrera, utvärdera och sammanfatta tidigare forskning tillsammans med vår egen forskning inom området för bildegenskaper. Denna avhandling presenterar ett ramverk för begrepp, utvärdering, utförande och applikationer för bilddetaljer. För det första föreslår denna avhandling ett nytt koncept för intressekurvor. Intressekurvor är ett begrepp som härrör från intressepunkter och det är viktiga linjer och bågar i bilden som är repeterbara oberoende av olika bildtransformationer. Intressekurvor ger en tydlig vägledning och struktur för framtida algoritmer och relaterade tillämpningar för kurv- och linjedetektering. För det andra, presenterar denna avhandling en utvärderingsram för detektorer och beskrivningar av intressekurvor. Utvärderingsramverket utgör en ny paradigm för att jämföra resultatet för de bästa möjliga teknikerna för linje- och kurvdetektorer vid bildstörningar och bildtransformationer. För det tredje presenterar denna avhandling en detektor för intressekurvor (Distinctive curves, DICU), som förenar detektering av kanter, hörn, linjer och kurvor. DICU representerar vårt främsta bidrag inom området detektering av kanter, hörn, kurvor och linjer. Våra forskningsinsatser täcker de viktigaste attribut som krävs av dessa funktioner med avseende på robusthet och effektivitet. Intressekurvor innehåller en rikare geometrisk information än intressepunkter. Denna fördel öppnar för nya sätt att lösa problem för datorseende. Vi föreslår en enkel beskrivningsmetod för kurvmatchningsapplikationer och den föreslagna deskriptorn för intressekurvor överträffar de bästa tillgängliga deskriptorerna för intressepunkter (SIFT, SURF, BRISK, ORB, och FREAK). Dessutom utformar vi en ny objektdetekteringsalgoritm som bara använder geometri för DICU utan att använda det lokala utseendet. Vi organiserar bildobjekt som kurvkedjor och för att upptäcka ett objekt behöver vi endast söka efter denna kurvkedja i målbilden med hjälp av dynamisk programmering. Kurvkedjematchningen är oberoende av skala och rotationer samt robust vid bilddeformationer. Dessa egenskaper ger möjlighet att lösa problemet med rotationsberoende inom objektdetektering. Vårt ansiktsigenkänningsexperiment visar att kurvkedjematchning är oberoende av skala och rotationer och att den är mycket beräkningseffektiv. INTRO – INteractive RObotics research network

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HE, LEI. "A COMPARISON OF DEFORMABLE CONTOUR METHODS AND MODEL BASED APPROACH USING SKELETON FOR SHAPE RECOVERY FROM IMAGES." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1059746287.

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Madi, Kamel. "Inexact graph matching : application to 2D and 3D Pattern Recognition." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE1315/document.

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Les Graphes sont des structures mathématiques puissantes constituant un outil de modélisation universel utilisé dans différents domaines de l'informatique, notamment dans le domaine de la reconnaissance de formes. L'appariement de graphes est l'opération principale dans le processus de la reconnaissance de formes à base de graphes. Dans ce contexte, trouver des solutions d'appariement de graphes, garantissant l'optimalité en termes de précision et de temps de calcul est un problème de recherche difficile et d'actualité. Dans cette thèse, nous nous intéressons à la résolution de ce problème dans deux domaines : la reconnaissance de formes 2D et 3D. Premièrement, nous considérons le problème d'appariement de graphes géométriques et ses applications sur la reconnaissance de formes 2D. Dance cette première partie, la reconnaissance des Kites (structures archéologiques) est l'application principale considérée. Nous proposons un "framework" complet basé sur les graphes pour la reconnaissance des Kites dans des images satellites. Dans ce contexte, nous proposons deux contributions. La première est la proposition d'un processus automatique d'extraction et de transformation de Kites a partir d'images réelles en graphes et un processus de génération aléatoire de graphes de Kites synthétiques. En utilisant ces deux processus, nous avons généré un benchmark de graphes de Kites (réels et synthétiques) structuré en 3 niveaux de bruit. La deuxième contribution de cette première partie, est la proposition d'un nouvel algorithme d'appariement pour les graphes géométriques et par conséquent pour les Kites. L'approche proposée combine les invariants de graphes au calcul de l'édition de distance géométrique. Deuxièmement, nous considérons le problème de reconnaissance des formes 3D ou nous nous intéressons à la reconnaissance d'objets déformables représentés par des graphes c.à.d. des tessellations de triangles. Nous proposons une décomposition des tessellations de triangles en un ensemble de sous structures que nous appelons triangle-étoiles. En se basant sur cette décomposition, nous proposons un nouvel algorithme d'appariement de graphes pour mesurer la distance entre les tessellations de triangles. L'algorithme proposé assure un nombre minimum de structures disjointes, offre une meilleure mesure de similarité en couvrant un voisinage plus large et utilise un ensemble de descripteurs qui sont invariants ou au moins tolérants aux déformations les plus courantes. Finalement, nous proposons une approche plus générale de l'appariement de graphes. Cette approche est fondée sur une nouvelle formalisation basée sur le problème de mariage stable. L'approche proposée est optimale en terme de temps d'exécution, c.à.d. la complexité est quadratique O(n2), et flexible en terme d'applicabilité (2D et 3D). Cette approche se base sur une décomposition en sous structures suivie par un appariement de ces structures en utilisant l'algorithme de mariage stable. L'analyse de la complexité des algorithmes proposés et l'ensemble des expérimentations menées sur les bases de graphes des Kites (réelle et synthétique) et d'autres bases de données standards (2D et 3D) attestent l'efficacité, la haute performance et la précision des approches proposées et montrent qu'elles sont extensibles et générales Graphs are powerful mathematical modeling tools used in various fields of computer science, in particular, in Pattern Recognition. Graph matching is the main operation in Pattern Recognition using graph-based approach. Finding solutions to the problem of graph matching that ensure optimality in terms of accuracy and time complexity is a difficult research challenge and a topical issue. In this thesis, we investigate the resolution of this problem in two fields: 2D and 3D Pattern Recognition. Firstly, we address the problem of geometric graphs matching and its applications on 2D Pattern Recognition. Kite (archaeological structures) recognition in satellite images is the main application considered in this first part. We present a complete graph based framework for Kite recognition on satellite images. We propose mainly two contributions. The first one is an automatic process transforming Kites from real images into graphs and a process of generating randomly synthetic Kite graphs. This allowing to construct a benchmark of Kite graphs (real and synthetic) structured in different level of deformations. The second contribution in this part, is the proposition of a new graph similarity measure adapted to geometric graphs and consequently for Kite graphs. The proposed approach combines graph invariants with a geometric graph edit distance computation. Secondly, we address the problem of deformable 3D objects recognition, represented by graphs, i.e., triangular tessellations. We propose a new decomposition of triangular tessellations into a set of substructures that we call triangle-stars. Based on this new decomposition, we propose a new algorithm of graph matching to measure the distance between triangular tessellations. The proposed algorithm offers a better measure by assuring a minimum number of triangle-stars covering a larger neighbourhood, and uses a set of descriptors which are invariant or at least oblivious under most common deformations. Finally, we propose a more general graph matching approach founded on a new formalization based on the stable marriage problem. The proposed approach is optimal in term of execution time, i.e. the time complexity is quadratic O(n2) and flexible in term of applicability (2D and 3D). The analyze of the time complexity of the proposed algorithms and the extensive experiments conducted on Kite graph data sets (real and synthetic) and standard data sets (2D and 3D) attest the effectiveness, the high performance and accuracy of the proposed approaches and show that the proposed approaches are extensible and quite general

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Baruzzi, Francesco. "Object detection in robot picking applications using 3d cameras." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22594/.

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This work relies on analysis and testing of two 3D cameras in robot picking applications. Continuous progress in microelectronics, micro optics and micro technology made 3D cameras affordable and competitive with respect to 2D cameras in common industrial and commercial applications. 3D cameras, in fact, could give advantages in terms of timing and performances in application involving objects normally processed with 2D cameras. These cameras are intrinsically different in the technology used and provide mono/color images, depth maps and point clouds. First camera considered, Intel RealSense D435, is a stereo camera and the second one, Basler blaze 101, is a time of flight camera. A particular focus is devoted to enhancing eventual advantages of using one technology with respect to the other. It’s important to underline that objects examined are quite small and heterogeneous in terms of material, opacity, colors, textures and transparency. Qualitative considerations will be done to a priori exclude from tests objects which depth maps result difficult to be created and exploited. Then, through computer vision algorithms, depth information are used to detect graspable objects and to get their positioning and orientations. These algorithms are capable of managing both isolated, touching or overlapping objects, establishing which one can be picked up and which not. Three different algorithms will be deeply introduced and described with a detailed focus on best camera settings to accomplish these tasks. Several analyses are done with both static and moving products on conveyor belts, varying conveyors colors and shapes.

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Sadek, Rida. "Some problems on temporally consistent video editing and object recognition." Doctoral thesis, Universitat Pompeu Fabra, 2012. http://hdl.handle.net/10803/101413.

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Video editing and object recognition are two significant fields in computer vi- sion: the first has remarkably assisted digital production and post-production tasks of a digital video footage; the second is considered fundamental to image classification or image based search in large databases (e.g. the web). In this thesis, we address two problems, namely we present a novel formulation that tackles video editing tasks and we develop a mechanism that allows to generate more robust descriptors for objects in an image. Concerning the first problem, this thesis proposes two variational models to perform temporally coherent video editing. These models are applied to change an object’s (rigid or non-rigid) texture throughout a given video sequence. One model is based on propagating color information from a given frame (or be- tween two given frames) along the motion trajectories of the video; while the other is based on propagating gradient domain information. The models we present in this thesis require minimal user intervention and they automatically accommodate for illumination changes in the scene. Concerning the second problem, this thesis addresses the problem of affine invariance in object recognition. We introduce a way to generate geometric affine invariant quantities that are used in the construction of feature descrip- tors. We show that when these quantities are used they do indeed achieve a more robust recognition than the state of the art descriptors. i La edición de vídeo y el reconocimiento de objetos son dos áreas fundamentales en el campo de la visión por computador: la primera es de gran utilidad en los procesos de producción y post-producción digital de vídeo; la segunda es esencial para la clasificación o búsqueda de imágenes en grandes bases de datos (por ejemplo, en la web). En esta tesis se acometen ambos problemas, en concreto, se presenta una nueva formulación que aborda las tareas de edición de vídeo y se desarrolla un mecanismo que permite generar descriptores más robustos para los objetos de la imagen. Con respecto al primer problema, en esta tesis se proponen dos modelos variacionales para llevar a cabo la edición de vídeo de forma coherente en el tiempo. Estos modelos se aplican para cambiar la textura de un objeto (rígido o no) a lo largo de una secuencia de vídeo dada. Uno de los modelos está basado en la propagación de la información de color desde un determinado cuadro de la secuencia de vídeo (o entre dos cuadros dados) a lo largo de las trayectorias de movimiento del vídeo. El otro modelo está basado en la propagación de la información en el dominio del gradiente. Ambos modelos requieren una intervención mínima por parte del usuario y se ajustan de manera automática a los cambios de iluminación de la escena. Con respecto al segundo problema, esta tesis aborda el problema de la invariancia afín en el reconocimiento de objetos. Se introduce un nuevo método para generar cantidades geométricas afines que se utilizan en la generación de descriptores de características. También se demuestra que el uso de dichas cantidades proporciona mayor robustez al reconocimiento que los descriptores existentes actualmente en el estado del arte.

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Revaud, Jérôme. "Contributions to a fast and robust object recognition in images." Phd thesis, INSA de Lyon, 2011. http://tel.archives-ouvertes.fr/tel-00694442.

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In this thesis, we first present a contribution to overcome this problem of robustness for the recognition of object instances, then we straightly extend this contribution to the detection and localization of classes of objects. In a first step, we have developed a method inspired by graph matching to address the problem of fast recognition of instances of specific objects in noisy conditions. This method allows to easily combine any types of local features (eg contours, textures ...) less affected by noise than keypoints, while bypassing the normalization problem and without penalizing too much the detection speed. Unlike other methods based on a global rigid transformation, our approach is robust to complex deformations such as those due to perspective or those non-rigid inherent to the model itself (e.g. a face, a flexible magazine). Our experiments on several datasets have showed the relevance of our approach. It is overall slightly less robust to occlusion than existing approaches, but it produces better performances in noisy conditions. In a second step, we have developed an approach for detecting classes of objects in the same spirit as the bag-of-visual-words model. For this we use our cascaded micro-classifiers to recognize visual words more distinctive than the classical words simply based on visual dictionaries. Training is divided into two parts: First, we generate cascades of micro-classifiers for recognizing local parts of the model pictures and then in a second step, we use a classifier to model the decision boundary between images of class and those of non-class. We show that the association of classical visual words (from keypoints patches) and our disctinctive words results in a significant improvement. The computation time is generally quite low, given the structure of the cascades that minimizes the detection time and the form of the classifier is extremely fast to evaluate.

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