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Tesi sul tema "Object invariants"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 19 febbraio 2022

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1

Self, T. Benjamin (Thomas Benjamin) 1977. "Expression and localization of object invariants." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86498.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (leaf 23).<br>by T. Benjamin Self.<br>S.B.and M.Eng.

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2

Vinther, Sven. "Active 3D object recognition using geometric invariants." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362974.

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3

Beis, Jeffrey S. "Indexing without invariants in model-based object recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq25014.pdf.

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4

Zhu, Yonggen. "Feature extraction and 2D/3D object recognition using geometric invariants." Thesis, King's College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362731.

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5

Soysal, Medeni. "Joint Utilization Of Local Appearance Descriptors And Semi-local Geometry For Multi-view Object Recognition." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614313/index.pdf.

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Novel methods of object recognition that form a bridge between today&rsquo<br>s local feature frameworks and previous decade&rsquo<br>s strong but deserted geometric invariance field are presented in this dissertation. The rationale behind this effort is to complement the lowered discriminative capacity of local features, by the invariant geometric descriptions. Similar to our predecessors, we first start with constrained cases and then extend the applicability of our methods to more general scenarios. Local features approach, on which our methods are established, is reviewed in three parts<br

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6

Wilhelm, Hedwig. "A Neural Network Model of Invariant Object Identification." Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-62050.

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Invariant object recognition is maybe the most basic and fundamental property of our visual system. It is the basis of many other cognitive tasks, like motor actions and social interactions. Hence, the theoretical understanding and modeling of invariant object recognition is one of the central problems in computational neuroscience. Indeed, object recognition consists of two different tasks: classification and identification. The focus of this thesis is on object identification under the basic geometrical transformations shift, scaling, and rotation. The visual system can perform shift, size

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7

Srestasathiern, Panu. "View Invariant Planar-Object Recognition." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1420564069.

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8

Tonge, Ashwini Kishor. "Object Recognition Using Scale-Invariant Chordiogram." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984116/.

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This thesis describes an approach for object recognition using the chordiogram shape-based descriptor. Global shape representations are highly susceptible to clutter generated due to the background or other irrelevant objects in real-world images. To overcome the problem, we aim to extract precise object shape using superpixel segmentation, perceptual grouping, and connected components. The employed shape descriptor chordiogram is based on geometric relationships of chords generated from the pairs of boundary points of an object. The chordiogram descriptor applies holistic properties of the sh

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9

Dahmen, Jörg. "Invariant image object recognition using Gaussian mixture densities." [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=964586940.

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10

Booth, Michael C. A. "Temporal lobe mechanisms for view-invariant object recognition." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299094.

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11

Hsu, Tao-i. "Affine invariant object recognition by voting match techniques." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/22865.

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Approved for public release; distribution is unlimited<br>This thesis begins with a general survey of different model based systems for object recognition. The advantage and disadvantage of those systems are discussed. A system is then selected for study because of its effective Affine invariant matching [Ref. 1] characteristic. This system involves two separate phases, the modeling and the recognition. One is done off-line and the other is done on-line. A Hashing technique is implemented to achieve fast accessing and voting. Different test data sets are used in experiments to illustra

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12

Kao, Chang-Lung. "Affine invariant matching of noisy objects." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/26852.

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13

Reiss, T. H. "Recognizing objects using invariant image features." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260553.

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14

Allan, Moray. "Sprite learning and object category recognition using invariant features." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/2430.

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This thesis explores the use of invariant features for learning sprites from image sequences, and for recognising object categories in images. A popular framework for the interpretation of image sequences is the layers or sprite model of e.g. Wang and Adelson (1994), Irani et al. (1994). Jojic and Frey (2001) provide a generative probabilistic model framework for this task, but their algorithm is slow as it needs to search over discretised transformations (e.g. translations, or affines) for each layer. We show that by using invariant features (e.g. Lowe’s SIFT features) and clustering their mo

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15

Bone, Peter. "Fully invariant object recognition and tracking from cluttered scenes." Thesis, University of Sussex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444109.

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16

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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17

Robinson, Leigh. "Invariant object recognition : biologically plausible and machine learning approaches." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/83167/.

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Understanding the processes that facilitate object recognition is a task that draws on a wide range of fields, integrating knowledge from neuroscience, psychology, computer science and mathematics. The substantial work done in these fields has lead to two major outcomes: Firstly, a rich interplay between computational models and biological experiments that seek to explain the biological processes that underpin object recognition. Secondly, engineered vision systems that on many tasks are approaching the performance of humans. This work first highlights the importance of ensuring models which a

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18

Romero, i. Sànchez David. "Numerical computation of invariant objects with wavelets." Doctoral thesis, Universitat Autònoma de Barcelona, 2015. http://hdl.handle.net/10803/395169.

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19

Zhang, Hui. "The investigation of correlator systems utilizing object and frequency space filters." Thesis, Abertay University, 2000. https://rke.abertay.ac.uk/en/studentTheses/5afbbda6-0d84-471b-bfcd-f3717c905233.

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The aim of this research is to develop real-time object recognition systems which are robust and have good discrimination. An important aspect of this work is the development of a rotationally invariant optical correlator. Optical correlation systems are investigated for the purpose of high speed, high discriminant and distortion invariant pattern recognition. A photorefractive joint transform correlator (JTC) using Bismuth Silicon Oxide (BSO) as a non-linear recording medium and a liquid crystal television as a spatial light modulator is implemented. The underlying physics is considered, some

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20

Weaver, Jon. "Naming familiar objects promotes viewpoint-invariance." Thesis, Lancaster University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404238.

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21

Graf, Thorsten. "Flexible object recognition based on invariant theory and agent technology." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=96086170X.

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22

Woo, Myung Chul. "Biologically-inspired translation, scale, and rotation invariant object recognition models /." Online version of thesis, 2007. http://hdl.handle.net/1850/3933.

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23

Perry, Gavin. "Computational models of invariant object representation in the inferotemporal cortex." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425893.

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24

Banarse, D. S. "A generic neural network architecture for deformation invariant object recognition." Thesis, Bangor University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362146.

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25

Li, Nuo Ph D. Massachusetts Institute of Technology. "Unsupervised learning of invariant object representation in primate visual cortex." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65288.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>Visual object recognition (categorization and identification) is one of the most fundamental cognitive functions for our survival. Our visual system has the remarkable ability to convey to us visual object and category information in a manner that is largely tolerant ("invariant") to the exact position, size, pose of the object, illumination, and clutter. The ventral visual stream in non-human primate has solved

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26

Mathew, Alex. "Rotation Invariant Histogram Features for Object Detection and Tracking in Aerial Imagery." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1397662849.

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27

Dias, Malcolm Benjamin. "Implicit, view-invariant modelling of 3D non-rigid objects." Thesis, University College London (University of London), 2004. http://discovery.ucl.ac.uk/1446859/.

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This thesis describes and evaluates the Integrated Shape and Pose Model (ISPM), a novel technique for modelling the geometry of a 3D non-rigid object, such as a face, via images captured from various viewpoints. The ISPM can be trained on almost any set of images since it does not require images captured simultaneously from more than one view. This is advantageous over conventional techniques that impose such constraints on the training data. The ISPM is built by transferring the object's intrinsic shape from the view of each training image, to two basis views. This is achieved by first comput

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28

Yadav, Kamna. "Improving Accuracy of the Edgebox Approach." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7326.

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Object region detection plays a vital role in many domains ranging from self-driving cars to lane detection, which heavily involves the task of object detection. Improving the performance of object region detection approaches is of great importance and therefore is an active ongoing research in Computer Vision. Traditional sliding window paradigm has been widely used to identify hundreds of thousands of windows (covering different scales, angles, and aspect ratios for objects) before the classification step. However, it is not only computationally expensive but also produces relatively low acc

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29

Eskizara, Omer. "3d Geometric Hashing Using Transform Invariant Features." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610546/index.pdf.

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3D object recognition is performed by using geometric hashing where transformation and scale invariant 3D surface features are utilized. 3D features are extracted from object surfaces after a scale space search where size of each feature is also estimated. Scale space is constructed based on orientation invariant surface curvature values which classify each surface point&#039<br>s shape. Extracted features are grouped into triplets and orientation invariant descriptors are defined for each triplet. Each pose of each object is indexed in a hash table using these triplets. For scale invariance m

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30

Riley, Timothy Rupert. "Asymptotic invariants of infinite discrete groups." Thesis, University of Oxford, 2002. http://ora.ox.ac.uk/objects/uuid:30f42f4c-e592-44c2-9954-7d9e8c1f3d13.

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<b>Asymptotic cones.</b> A finitely generated group has a word metric, which one can scale and thereby view the group from increasingly distant vantage points. The group coalesces to an "asymptotic cone" in the limit (this is made precise using techniques of non-standard analysis). The reward is that in place of the discrete group one has a continuous object "that is amenable to attack by geometric (e.g. topological, infinitesimal) machinery" (to quote Gromov). We give coarse geometric conditions for a metric space X to have N-connected asymptotic cones. These conditions are expressed in terms

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31

Ghedin, Emanuele. "Rational Cherednik algebras and link invariants." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:409475e8-ef3b-490a-8973-d2b2d52b2f5e.

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Motivated by homological mirror symmetry, Smith and Thomas tried to construct a link invariant considering the derived category of coherent sheaves on the Hilbert scheme of n points on the minimal resolution of the Klenian singularity of type A, and an object L(n) thereof. The braid group acts on this category by spherical twists, so one obtains a braid invariant by taking the Ext between L(n) and its image under the braid group action. Smith and Thomas proved that taking the plat closure of the braid, this cohomology does not produce a link invariant but is close to doing so, and they conject

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32

Voils, Danny. "Scale Invariant Object Recognition Using Cortical Computational Models and a Robotic Platform." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/632.

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This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the visual field. The proposed model combines Numenta's Hierarchical Temporal Memory (HTM), with HMAX developed by MIT's Brain and Cognitive Science Department. While these two biologically inspired paradigms a

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33

Leung, Wai-Man Raymond. "On spin c-invariants of four-manifolds." Thesis, University of Oxford, 1995. http://ora.ox.ac.uk/objects/uuid:a9790f36-748f-4574-a97c-4f416ca67207.

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The spin<sup>c</sup>-invariants for a compact smooth simply-connected oriented four-manifold, as defined by Pidstrigach and Tyurin, are studied in this thesis. Unlike the Donaldson polynomial invariants, they are defined by cutting down the moduli space M' of '1-instantons', which is the subspace of the moduli space M of anti-self-dual connections parametrizing coupled (spin<sup>c</sup>) Dirac operators with non-trivial kernel. Our main goal is to study the relationship between these spin<sup>c</sup>-invariants and the Donaldson polynomial invariants. The 'jumping subset' M' defined a cohomolo

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34

Evans, Benjamin D. "Learning transformation-invariant visual representations in spiking neural networks." Thesis, University of Oxford, 2012. https://ora.ox.ac.uk/objects/uuid:15bdf771-de28-400e-a1a7-82228c7f01e4.

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This thesis aims to understand the learning mechanisms which underpin the process of visual object recognition in the primate ventral visual system. The computational crux of this problem lies in the ability to retain specificity to recognize particular objects or faces, while exhibiting generality across natural variations and distortions in the view (DiCarlo et al., 2012). In particular, the work presented is focussed on gaining insight into the processes through which transformation-invariant visual representations may develop in the primate ventral visual system. The primary motivation for

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35

Weismantel, Eric. "Perceptual Salience of Non-accidental Properties." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1376610211.

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36

Isik, Leyla. "The dynamics of invariant object and action recognition in the human visual system." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98000.

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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 123-138).<br>Humans can quickly and effortlessly recognize objects, and people and their actions from complex visual inputs. Despite the ease with which the human brain solves this problem, the underlying computational steps have remained enigmatic. What makes object and action recognition challenging are identity-preserving transformations that alter the visual appearance of objects and actions, such as c

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37

Christou, Alexis. "Dynamics on scale-invariant structures." Thesis, University of Oxford, 1987. http://ora.ox.ac.uk/objects/uuid:15fd6e54-0ac4-4f4d-8115-0ee51ad74504.

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We investigate dynamical processes on random and regular fractals. The (static) problem of percolation in the semi-infinite plane introduces many pertinent ideas including real space renormalisation group (RSRG) fugacity transformations and scaling forms. We study the percolation probability to determine the surface critical behaviour and to establish exponent relations. The fugacity approach is generalised to study random walks on diffusion-limited aggregates (DLA). Using regular and random models, we calculate the walk dimensionality and demonstrate that it is consistent with a conjecture by

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38

Li, Muhua 1973. "Learning invariant neuronal representations for objects across visual-related self-actions." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85565.

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This work is aimed at understanding and modelling the perceptual stability mechanisms of human visual systems, regardless of large changes in the visual sensory input resulting from some visual-related motions. Invariant neuronal representation plays an important role for memory systems to associate and recognize objects.<br>In contrast to the bulk of previous research work on the learning of invariance that focuses on the pure bottom-up visual information, we incorporate visual-related self-action signals such as commands for eye, head or body movements, to actively collect the changin

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39

Baris, Yuksel. "Automated Building Detection From Satellite Images By Using Shadow Information As An Object Invariant." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614909/index.pdf.

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Apart from classical pattern recognition techniques applied for automated building detection in satellite images, a robust building detection methodology is proposed, where self-supervision data can be automatically extracted from the image by using shadow and its direction as an invariant for building object. In this methodology<br>first the vegetation, water and shadow regions are detected from a given satellite image and local directional fuzzy landscapes representing the existence of building are generated from the shadow regions using the direction of illumination obtained from image meta

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40

Nelson, Eric D. "Zoom techniques for achieving scale invariant object tracking in real-time active vision systems /." Online version of the thesis, 2006. https://ritdml.rit.edu/dspace/handle/1850/2620.

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41

Zografos, V. "Pose-invariant, model-based object recognition, using linear combination of views and Bayesian statistics." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/18954/.

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This thesis presents an in-depth study on the problem of object recognition, and in particular the detection of 3-D objects in 2-D intensity images which may be viewed from a variety of angles. A solution to this problem remains elusive to this day, since it involves dealing with variations in geometry, photometry and viewing angle, noise, occlusions and incomplete data. This work restricts its scope to a particular kind of extrinsic variation; variation of the image due to changes in the viewpoint from which the object is seen. A technique is proposed and developed to address this problem, wh

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42

Rahtu, E. (Esa). "A multiscale framework for affine invariant pattern recognition and registration." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514286018.

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Abstract This thesis presents a multiscale framework for the construction of affine invariant pattern recognition and registration methods. The idea in the introduced approach is to extend the given pattern to a set of affine covariant versions, each carrying slightly different information, and then to apply known affine invariants to each of them separately. The key part of the framework is the construction of the affine covariant set, and this is done by combining several scaled representations of the original pattern. The advantages compared to previous approaches include the possibility of

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43

Huguet, Casades Gemma. "The role of hyperbolic invariant objects: From Arnold diffusion to biological clocks." Doctoral thesis, Universitat Politècnica de Catalunya, 2008. http://hdl.handle.net/10803/5856.

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El marc d'aquesta tesi són els objectes invariants hiperbòlics (tors amb bigotis, cicles límit, NHIM,. . .), que constitueixen, per aquesta tesi, els objectes essencials per a l'estudi de diversos problemes des de la difusió d'Arnold fins als rellotges biològics. Treballem en tres temes diferents des d'un enfocament tant teòric com numèric, amb una especial atenció per a les aplicacions, especialment en neurobiologia:<br/>· Existència de difusió d'Arnold per a sistemes Hamiltonians a priori inestables<br/>· Algorismes numèrics ràpids per al càlcul de tors invariants i els "bigotis" associats,

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44

Hall, Daniela. "Viewpoint independent recognition of objects from local appearance." Grenoble INPG, 2001. http://www.theses.fr/2001INPG0086.

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45

Baston, Robert J. "The algebraic construction of invariant differential operators." Thesis, University of Oxford, 1985. http://ora.ox.ac.uk/objects/uuid:a7cb5790-7267-47d2-9179-df705405ae08.

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Let G be a complex semisimple Lie Group with parabolic subgroup P, so that G/P is a generalized flag manifold. An algebraic construction of invariant differential operators between sections of homogeneous bundles over such spaces is given and it is shown how this leads to the classification of all such operators. As an example of a process which naturally generates such operators, the algebraic Penrose transform between generalized flag manifolds is given and computed for several cases, extending standard results in Twistor Theory to higher dimensions. It is then shown how to adapt the homogen

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46

Umasuthan, M. "Recognition and position estimation of 3D objects from range images using algebraic and moment invariants." Thesis, Heriot-Watt University, 1995. http://hdl.handle.net/10399/763.

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47

Glauser, Thomas. "CAD-based recognition of polyhedral 3-D objects using affine invariant surface representations /." Bern : Universität Bern Institut für Informatik und angewandte Mathematik, 1992. http://www.ub.unibe.ch/content/bibliotheken_sammlungen/sondersammlungen/dissen_bestellformular/index_ger.html.

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48

Smart, Michael Howard William. "Adaptive, linear, subspatial projections for invariant recognition of objects in real infrared images." Thesis, University of Edinburgh, 1998. http://hdl.handle.net/1842/12974.

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In recent years computer technology has advanced to a state whereby large quantities of data can be processed. This advancement has fuelled a dramatic increased in research into areas of image processing which were previously impractical, such as automated vision systems for, both military, and domestic purposes. Automatic Target Recognition (ATR) systems are one such example of these automated processes. ATR is the automatic detection, isolation and identification of objects, often derived from raw video, in a real-world, potentially hostile environment. The ability to rapidly, and accurately

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49

Donatti, Guillermo Sebastián [Verfasser], Rolf [Gutachter] Würtz, and Boris [Gutachter] Suchan. "Memory organization for invariant object recognition and categorization / Guillermo Sebastián Donatti ; Gutachter: Rolf Würtz, Boris Suchan." Bochum : Ruhr-Universität Bochum, 2016. http://d-nb.info/1114496944/34.

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Ojansivu, V. (Ville). "Blur invariant pattern recognition and registration in the Fourier domain." Doctoral thesis, University of Oulu, 2009. http://urn.fi/urn:isbn:9789514292552.

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Abstract Pattern recognition and registration are integral elements of computer vision, which considers image patterns. This thesis presents novel blur, and combined blur and geometric invariant features for pattern recognition and registration related to images. These global or local features are based on the Fourier transform phase, and are invariant or insensitive to image blurring with a centrally symmetric point spread function which can result, for example, from linear motion or out of focus. The global features are based on the even powers of the phase-only discrete Fourier spectrum or

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