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Dissertations / Theses on the topic 'Model-based recognition'
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1
Zhou, Ziheng. "Model-based gait extraction and recognition." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438517.
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Lauziere, Yves Berude. "A model-based road sign recognition system /." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=38151.
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A road sign recognition system poses a real challenge for machine vision. It must recognize a wide variety of road signs under considerable variations in illumination and imaging geometry---all in real-time. This thesis presents a modular road sign recognition system relying on modelling for both detection and recognition. It divides into three main stages of processing. The first, concerned with detection, exploits the specific colors of road signs. The color constancy problem caused by the daylight illumination variations is addressed directly with a physics-based model supplemented by a calibration stage using real data. The second stage of processing, devoted to recognizing road signs in regions of interest found in the detection phase, involves a database containing more than 400 road signs arranged in a tree structure, and uses a novel correlation-based template matching technique relying on a bitwise encoding that accounts for both color labels and affine variations in the image formation process, and which also allows to build templates that are able to represent classes of objects. The content of the database used by the recognition algorithm is generated in a deterministic and automated manner by way of geometrical modelling of the image formation process starting with only model images of the road signs to be recognized. The recognition algorithm exploits color as a first logical classification step to direct the search for a road sign in the database, with the later finer steps being driven by correlation scores obtained from template matching. At the third stage of processing, a scene understanding module exploits constraints on the position of road signs along with the spatial relationships they must have in certain cases to other road signs in the image to filter out false positives. During processing, the system incorporates top-down mechanisms that use data fed back by partial recognitions, which allow to progressively gain more information about
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Beattie, Valerie L. "Hidden Markov Model state-based noise compensation." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259519.
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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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Gales, Mark John Francis. "Model-based techniques for noise robust speech recognition." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319311.
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黃業新 and Yip-san Wong. "A two-level model-based object recognition technique." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31213807.
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Wong, Yip-san. "A two-level model-based object recognition technique /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14705552.
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Correa, Telmo Luis Jr. "A model for transition-based visuospatial pattern recognition." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66411.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 87).
In my research, I designed and implemented a system for learning and recognizing visual actions based on state transitions. I recorded three training videos of each of 16 actions (approach, bounce, carry, catch, collide, drop, fly over, follow, give, hit, jump, pick, push, put, take, throw), each lasting 10 seconds and 300 frames. After using a prototype system developed by Dr. Satyajit Rao for focus and actor recognition, actions are represented as qualitative state transitions, tied together to form tens of thousands of patterns, which are then available as action classifiers. The resulting system was able to build simple, intuitive classifiers that fit the training data perfectly.
by Telmo Luis Correa Junior.
M.Eng.
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Crawford, Gordon Finlay. "Vision-based analysis, interpretation and segmentation of hand shape using six key marker points." Thesis, University of Ulster, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243732.
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Oshitani, Tohru, and Toyohide Watanabe. "Parallel map recognition based on multilayer partitioned blackboard model." IEEE, 1998. http://hdl.handle.net/2237/6916.
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Kuhn, Roland. "A cache-based natural language model for speech recognition /." Thesis, McGill University, 1988. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61941.
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Yeung, Stephen Siu Kau. "Model-based tactile object recognition using pseudo-random encoding." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq21021.pdf.
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Pilu, Maurizio. "Part-based grouping and recognition : a model-guided approach." Thesis, University of Edinburgh, 1996. http://hdl.handle.net/1842/569.
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The recovery of generic solid parts is a fundamental step towards the realization of general-purpose vision systems. This thesis investigates issues in grouping, segmentation and recognition of parts from two-dimensional edge images. A new paradigm of part-based grouping of features is introduced that bridges the classical grouping and model-based approaches with the purpose of directly recovering parts from real images, and part-like models are used that both yield low theoretical complexity and reliably recover part-plausible groups of features. The part-like models used are statistical point distribution models, whose training set is built using random deformable superellipse. The computational approach that is proposed to perform model-guided part-based grouping consists of four distinct stages. In the first stage, codons, contour portions of similar curvature, are extracted from the raw edge image. They are considered to be indivisible image features because they have the desirable property of belonging either to single parts or joints. In the second stage, small seed groups (currently pairs, but further extension are proposed) of codons are found that give enough structural information for part hypotheses to be created. The third stage consists in initialising and pre-shaping the models to all the seed groups and then performing a full fitting to a large neighbourhood of the pre-shaped model. The concept of pre-shaping to a few significant features is a relatively new concept in deformable model fitting that has helped to dramatically increase robustness. The initialisations of the part models to the seed groups is performed by the first direct least-square ellipse fitting algorithm, which has been jointly discovered during this research; a full theoretical proof of the method is provided. The last stage pertains to the global filtering of all the hypotheses generated by the previous stages according to the Minimum Description Length criterion: the small number of grouping hypotheses that survive this filtering stage are the most economical representation of the image in terms of the part-like models. The filtering is performed by the maximisation of a boolean quadratic function by a genetic algorithm, which has resulted in the best trade-off between speed and robustness. Finally, images of parts can have a pronounced 3D structure, with ends or sides clearly visible. In order to recover this important information, the part-based grouping method is extended by employing parametrically deformable aspects models which, starting from the initial position provided by the previous stages, are fitted to the raw image by simulated annealing. These models are inspired by deformable superquadrics but are built by geometric construction, which render them two order of magnitudes faster to generate than in previous works. A large number of experiments is provided that validate the approach and, since several new issues have been opened by it, some future work is proposed.
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Cunado, David. "Automatic gait recognition via model-based moving feature analysis." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297628.
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Bazzi, Louay Mohamad Jamil 1974. "Robust algorithms for model-based object recognition and localization." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9440.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 86-87).
We consider the problem of model-based object recognition and localization in the presence of noise, spurious features, and occlusion. We address the case where the model is allowed to be transformed by elements in a given space of allowable transformations. Known algorithms for the problem either treat noise very accurately in an unacceptable worst case running time, or may have unreliable output when noise is allowed. We introduce the idea of tolerance which measures the robustness of a recognition and localization method when noise is allowed. We present a collection of algorithms for the problem, each achieving a different degree of tolerance. The main result is a localization algorithm that achieves any desired tolerance in a relatively low order worst case asymptotic running time. The time constant of the algorithm depends on the ratio of the noise bound over the given tolerance bound. The solution we provide is general enough to handle different cases of allowable transformations, such as planar affine transformations, and scaled rigid motions in arbitrary dimensions.
by Louay Mohamad Jamil Bazzi.
S.M.
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Procter, Stephen. "Model-based polyhedral object recognition using edge-triple features." Thesis, University of Surrey, 1998. http://epubs.surrey.ac.uk/843142/.
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While significant progress has been made in the computer vision field over the past decade, and machines capable of performing specialised visual inspection tasks are now being used in many industrial applications, the problem of recognising three-dimensional objects from two-dimensional imagery remains an area of ongoing research. Vision is undoubtedly our most important sense, and solutions to the problem of general three-dimensional machine vision must be found if the long term goal of autonomous robotic agents interacting naturally with humans in the real world is to be realised. In this work the problem of recognising polyhedra from two-dimensional images is investigated. The use of perceptual grouping and intermediate-level geometric features is considered, in particular the "edge-triple" feature. The edge-triple feature consists of three connected straight edges of an object, projecting to a triple of connected lines in the image, and can be used as a key feature, or indexing primitive, in model-based object recognition. The geometric constraints provided by matching such a configuration of image lines to an edge-triple are sufficient to uniquely determine the pose of the object. A probabilistic analysis of the edge-triple feature is performed, and a method for computing the probability densities of the angles formed by the projections of object edges under parallel projection is developed. These probabilities are used to prioritise the processing of potential model/scene feature matches produced by the hypothesis generation stage of a polyhedral object recognition system, substantially increasing the efficiency of the verification stage of the recognition while imposing negligible computational and storage penalties on the method. A new polyhedral object recognition system based on geometric hashing is implemented using edge-triple features. The method relies on extensive preprocessing of object models to encode invariant object data in a hash table. By performing as much of the object analysis as possible off-line, the efficiency of the actual recognition stage is maximised, at the expense of heavy demands on memory due to the large amount of data stored in the hash tables. However, the memory requirements of our edge-triple method are lower than those of conventional geometric hashing algorithms. Additionally, since our method employs lines and line groupings as key features rather than sets of interest points, our method is less susceptible to noise in the imaging and feature extraction stages than conventional geometric hashing. The validity of these assertions is demonstrated by extensive testing and evaluation of the method using both synthetic and real image data. It is demonstrated that the accuracy of pose estimates produced by the method is commensurate with theoretical predictions based on algorithm parameters and the expected errors in the image feature extraction. Since the projection from three-dimensional space to a single two-dimensional image necessarily involves a loss of information, the question of combining information from several images is addressed. A multi-view viewpoint consistency constraint is proposed, enabling the compatibility of recognition hypotheses from several viewpoints to be confirmed prior to the computationally expensive pose determination stage. The extra constraints provided by a multi-view analysis increase the reliability and robustness of the recognition system, while the consistency constraint helps to maintain the efficiency of the system. An active method to determine the complete three-dimensional structure of an edge-triple feature from two images is described. Finally, the limitations of the methods proposed and potential solutions to these shortcomings are discussed. Potential directions for future research are proposed.
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Dehmeshki, Jamshid. "Stochastic model-based approach to image analysis." Thesis, University of Nottingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363908.
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Du, Li. "The viewpoint consistency constraint in model-based vision." Thesis, University of Reading, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317162.
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Cavalin, Paulo Rodrigo. "Adaptive systems for hidden Markov model-based pattern recognition systems." Mémoire, École de technologie supérieure, 2011. http://espace.etsmtl.ca/976/1/CAVALIN_Paulo_Rodrigo.pdf.
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Cette thèse porte sur l’étude des systèmes adaptatifs pour la reconnaissance de formes. Habituellement
les systèmes de reconnaissance reposent sur une connaissance statique du problème
à résoudre et cela pour la durée de vie du système. Cependant il y a des circonstances où la
connaissance du problème est partielle lors de l’apprentissage initial à l’étape de la conception.
Pour cette raison, les systèmes de classification adaptatifs de nouvelle génération permettent
au système de base de s’adapter à la fois en apprenant sur les nouvelles données et sont également
capables de s’adapter à l’environnement lors de la généralisation. Cette thèse propose
une nouvelle définition d’un système de reconnaissance adaptatif où les MMCs (Modèles de
Markov Cachés) sont considérés comme étude de cas.
La première partie de la thèse présente une évaluation des principaux algorithmes d’apprentissage
incrémental utilisés pour l’estimation des paramètres des MMCs. L’objectif de cette étude
est de dégager les stratégies d’apprentissage incrémental dont la performance en généralisation
se rapproche de cette obtenue avec un apprentissage hors-ligne (batch). Les résultats obtenus
sur le problème de la reconnaissance de chiffres et de lettres manuscrits montrent la supériorité
des approches basées sur les ensembles de modèles. De plus, nous avons montré l’importance
de conserver dans une mémoire à court terme des exemples utilisés en validation, ce qui permet
d’obtenir un niveau de performance qui peut même dépasser celui obtenu en mode batch.
La deuxième partie de cette thèse est consacrée à la formulation d’une nouvelle approche pour
la sélection dynamique des ensembles de classifieurs. Inspiré du concept de fusion appelé
« organisation multi-niveau » (multistage organizations), nous avons formulé une variante de
ce concept appelé DMO (dynamic multistage organization - DMO) qui permet d’adapter la
fonction de fusion dynamiquement pour chaque exemple de test à classer. De plus, le concept
DMO a été intégré à la méthode DSA proposée par Dos Santos et al pour la sélection
dynamique d’ensembles de classifieurs. Ainsi, deux nouvelles variantes, DSAm et DSAc, ont
été proposées et évaluées. Dans le premier cas (DSAm), plusieurs fonctions de sélection permettent
une généralisation de la structure DMO. Pour ce qui est de la variante DSAc, nous
utilisons l’information contextuelle (représentée par les profils de décisions des classifieurs de
base) acquise par le système et qui est associée à la base de validation conservée dans une mémoire
à court terme. L’évaluation des deux approches sur des bases de données de petite et de
grande échelle ont montré que la méthode DSAc domine DSAm sur la plupart des cas étudiés.
Ce résultat montre que l’utilisation d’informations contextuelles permet une meilleure performance
en généralisation comparées aux méthodes non informées. Une propriété importante de
l’approche DSAc est qu’elle peut également servir pour apprendre de nouvelles données dans
le temps, une propriété très importante pour la conception de systèmes de reconnaissance adaptatifs dans les environnements dynamiques caractérisés par un niveau important d’incertitude
sur le problème à résoudre.
Finalement, un nouveau framework appelé LoGID (Local and Global Incremental Learning for
Dynamic Selection) est proposé pour la conception d’un système de reconnaissance adaptatif
basé sur les MMC, et capable de s’adapter dans le temps durant les phases d’apprentissage de
généralisation. Le système est composé d’un pool de classifieurs de base et l’adaptation durant
la phase de généralisation est effectuée par la sélection dynamique des membres du pool les
plus compétents pour classer chaque exemple de test. Le mécanisme de sélection dynamique
est basé sur l’algorithme des K plus proches vecteurs de décision, tandis que l’adaptation durant
la phase d’apprentissage consiste à la mise à jour et à l’ajout de classifieurs de base dans
le système. Durant la phase d’apprentissage, deux stratégies sont proposées pour apprendre
incrémentalement sur des nouvelles données: l’apprentissage local et l’apprentissage global.
L’apprentissage incrémentale local implique la mise à jour du pool de classifieurs de base en
ajoutant des nouveaux membres à cet ensemble. Les nouveaux membres sont générés avec
l’algorithme Learn++. L’apprentissage incrémental global consiste à la mise à jour de la base
de connaissances composée des vecteurs de décisions qui seront utilisés en généralisation pour
la sélection dynamique des membres les plus compétents.
Le système LoGID a été validé sur plusieurs bases de données et les résultats comparés à
ceux publiés dans la littérature. En général, la méthode proposée domine les autres méthodes
incluant les méthodes d’apprentissage hors-ligne. Enfin, le système LoGID évalué en mode
adaptatif montre qu’il est en mesure d’apprendre de nouvelles connaissances dans le temps au
moment où les nouvelles données sont disponibles. Cette faculté d’adaptation est très importante
également lorsque les données disponibles pour l’apprentissage sont peu nombreuses.
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Ibrayev, Rinat. "Model-based recognition of curves and surfaces using tactile data." [Ames, Iowa : Iowa State University], 2008.
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Benn, David E. "Model-based feature extraction and classification for automatic face recognition." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324811.
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Wang, Yongqiang. "Model-based approaches to robust speech recognition in diverse environments." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709461.
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Pattison, David Thomas. "A new heuristic-based model of goal recognition without libraries." Thesis, University of Strathclyde, 2015. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=25454.
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Goal Recognition concerns the problem of determining an agent's final goal, deduced from the plan they are currently executing (and subsequently being observed). For over twenty years, the de facto standard in plan and goal recognition has been to map an agent's observations to a set of known, valid and sound plans held within a plan library. In this time many novel techniques have been applied to the recognition problem, but almost all have relied on the presence of a library in some form or another. The work presented in this thesis advances the state-of-the-art in goal recognition by removing the need for any plan or goal library. Such libraries are tedious to construct, incomplete if done by hand, and possibly contain erroneous or irrelevant entries when done by machine. This work presents a new formulation of the recognition problem based on planning, which removes the need for such a structure to be present. This greatly widens the scenarios in which goal recognition can be realistically performed. While this new formalism overcomes many of the problems associated with traditional recognition research, it remains compatible with many of the concepts found in previous recognition work. This new defnition is first defined in the context of a rational agent and observer, before several relaxations are introduced which enable tractable goal recognition. This relaxed implementation is then extensively evaluated with regard to multiple aspects of the recognition problem.
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Ström, Jacob. "Model-based head tracking and coding /." Linköping : Univ, 2002. http://www.bibl.liu.se/liupubl/disp/disp2002/tek733s.pdf.
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Nefian, Ara. "A hidden Markov model-based approach for face detection and recognition." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/13888.
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Maier, Viktoria. "Temporal Episodic Memory Model : Towards Proactive Case-based Automatic Speech Recognition." Thesis, University of Sheffield, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522485.
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Sauer, Patrick Martin. "Model-based understanding of facial expressions." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/modelbased-understanding-of-facial-expressions(e88bff4f-d72e-4d11-b964-fc20f009609b).html.
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In this thesis we present novel methods for constructing and fitting 2d models of shape and appearance which are used for analysing human faces. The first contribution builds on previous work on discriminative fitting strategies for active appearance models (AAMs) in which regression models are trained to predict the location of shapes based on texture samples. In particular, we investigate non-parametric regression methods including random forests and Gaussian processes which are used together with gradient-like features for shape model fitting. We then develop two training algorithms which combine such models into sequences, and systematically compare their performance to existing linear generative AAM algorithms. Inspired by the performance of the Gaussian process-based regression methods, we investigate a group of non-linear latent variable models known as Gaussian process latent variable models (GPLVM). We discuss how such models may be used to develop a generative active appearance model algorithm whose texture model component is non-linear, and show how this leads to lower-dimensional models which are capable of generating more natural-looking images of faces when compared to equivalent linear models. We conclude by describing a novel supervised non-linear latent variable model based on Gaussian processes which we apply to the problem of recognising emotions from facial expressions.
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Ertas, Figen. "A correlogram approach to speaker identification based on a human auditory model." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390091.
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He, Xiaodong. "Model selection based speaker adaptation and its application to nonnative speech recognition /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3115555.
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Zhang, Shujun. "Model-based 3D object perception from single monochromatic images of unknown environments." Thesis, University of Reading, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315501.
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Menlove, Kit J. "Model Detection Based upon Amino Acid Properties." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2253.
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Similarity searches are an essential component to most bioinformatic applications. They form the bases of structural motif identification, gene identification, and insights into functional associations. With the rapid increase in the available genetic data through a wide variety of databases, similarity searches are an essential tool for accessing these data in an informative and productive way. In our chapter, we provide an overview of similarity searching approaches, related databases, and parameter options to achieve the best results for a variety of applications. We then provide a worked example and some notes for consideration. Homology detection is one of the most basic and fundamental problems at the heart of bioinformatics. It is central to problems currently under intense investigation in protein structure prediction, phylogenetic analyses, and computational drug development. Currently discriminative methods for homology detection, which are not readily interpretable, are substantially more powerful than their more interpretable counterparts, particularly when sequence identity is very low. Here I present a computational graph-based framework for homology inference using physiochemical amino acid properties which aims to both reduce the gap in accuracy between discriminative and generative methods and provide a framework for easily identifying the physiochemical basis for the structural similarity between proteins. The accuracy of my method slightly improves on the accuracy of PSI-BLAST, the most popular generative approach, and underscores the potential of this methodology given a more robust statistical foundation.
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Körner, Marco [Verfasser]. "Methods for Model-based and Model-free Recognition of Articulated Actions in Multi-View Environments / Marco Körner." München : Verlag Dr. Hut, 2015. http://d-nb.info/1079768017/34.
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Miranda, Maria Ausenda Carvalhal Leão Solha de. "3D Model-Based Recognition." Dissertação, 2016. https://repositorio-aberto.up.pt/handle/10216/90123.
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O objetivo deste trabalho é apresentar um estudo comparativo de algoritmos de reconhecimento de objetos 3D. Inicialmente, foi feito um estudo teórico das diferentes estratégias existentes. De seguida, foram implementados e testados alguns destes algoritmos. De modo a obter uma avaliação detalhada, foram efectuados testes num conjunto de dados sintéticos e, posteriormente, num conjunto de dados reais.
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Miranda, Maria Ausenda Carvalhal Leão Solha de. "3D Model-Based Recognition." Master's thesis, 2016. https://repositorio-aberto.up.pt/handle/10216/90123.
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O objetivo deste trabalho é apresentar um estudo comparativo de algoritmos de reconhecimento de objetos 3D. Inicialmente, foi feito um estudo teórico das diferentes estratégias existentes. De seguida, foram implementados e testados alguns destes algoritmos. De modo a obter uma avaliação detalhada, foram efectuados testes num conjunto de dados sintéticos e, posteriormente, num conjunto de dados reais.
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"Computational limitations of model based recognition." Massachusetts Institute of Technology, Laboratory for Information and Decision Systems, 1991. http://hdl.handle.net/1721.1/1203.
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Haim Shvaytser (Schweitzer), Sanjeev R. Kulkarni.Includes bibliographical references (p. 13-14).
Cover title.
Research supported by the U.S. Army Research Office. DAAL03-86-K-0171 Research supported by the Office of Naval Research under an Air Force Contract. F196128-90-C-0002
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Jacobs, D. W., and T. D. Alter. "Uncertainty Propagation in Model-Based Recognition." 1995. http://hdl.handle.net/1721.1/7337.
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Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three- dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three- dimensional objects, robust implementations of alignmentt interpretation- tree search, and ransformation clustering.
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Lin, Ruei-Min, and 林睿敏. "Pitch Recognition Based on Cochlear Model." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/06933429572949781312.
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碩士國立臺灣大學
電機工程學研究所
94
In this paper, an algorithm for pitch recognition is designed. This algorithm is based on a simplified cochlear model. The traditional methods are mainly divided into two categories: one is to utilize and analyze the amplitude of sound in time domain directly; the other is to transform the sound into the frequency domain first, and then do some analysis to recognize the pitch. The operation amount in time domain is relatively small, but mostly it can only detect a single frequency. The second type of methods needs to do the transform first, so the speed is relatively slow. After getting the frequency spectrum, we can apply some algorithm to do the pitch recognition. My algorithm, which is called CM (Cochlear Model), combines the advantages of above-mentioned two kinds of methods. CM utilizes the amplitude of sound directly. Through the simple cochlea physical model, the vibration situation of the BM(basement membrane) in the cochlea can tell the pitch. For the elasticity in the BM is not uniform, we can tell more than one single frequency at the same time.
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Chen, Juifeng, and 陳瑞豐. "Model-based Recognition for Mobile Robot." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/20829977276781418805.
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碩士國立中央大學
資訊工程研究所
87
A mobile robot navigation system includes three principal strategies of works, self-positioning, path finding, recognition of objects. Recognition approaches that achieve both self-positioning and recognition of objects are most point of this thesis. The idea of representation invariant to viewpoint changes is proposed. Some methods generated from it are employed to recognize objects. With the thought of model-based representation, landmarks, each of which is composed of some features, can be modeled to recognition functions. A recognition function evaluates to zero when the matching model of landmark is its input. After Gaussian noise is concerned, the recognition interval is derived. When the value of recognition function is within this interval, the recognition result is positive. Recognition of landmarks enables a mobile robot to position itself by the relationship in location. The goal of another method is to recognize places. The model of a place contains several sets of segments, each of which is a pair of segments. The constraint search accelerates a robot to find recognized models from the image features. The constraints include three layers of geometric characteristics of image features. One of them is a constraint of two segments, which forms a recognition function. With interference of nature noise, the output of the recognition function becomes an interval of real number. The threshold cannot be computed without prior probability of coordinates of segments. A hypothetical value of the threshold is assumed in experiments. The experiment is implemented in a simulation system and coded in Microsoft visual C++. A figure shows the statistic data, and a range of threshold value is proved to be optimal. Some revisions are made to reduce errors and computing time. The future work focuses on three directions, the ignorance of occlusion, autonomous modeling, and the problem of next viewpoint for searching.
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Moses, Yael, and Shimon Ullman. "Limitations of Non Model-Based Recognition Schemes." 1991. http://hdl.handle.net/1721.1/6571.
Full textAbstract:
Different approaches to visual object recognition can be divided into two general classes: model-based vs. non model-based schemes. In this paper we establish some limitation on the class of non model-based recognition schemes. We show that every function that is invariant to viewing position of all objects is the trivial (constant) function. It follows that every consistent recognition scheme for recognizing all 3-D objects must in general be model based. The result is extended to recognition schemes that are imperfect (allowed to make mistakes) or restricted to certain classes of objects.
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Cass, Todd A. "Robust 2-D Model-Based Object Recognition." 1988. http://hdl.handle.net/1721.1/6823.
Full textAbstract:
Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust hypothesis of feature matchings and transformations is considered. Bounds on the error in the image feature geometry are assumed constraining possible matchings and transformations. Transformation sampling is introduced as a simple, robust, polynomial-time, and highly parallel method of searching the space of transformations to hypothesize feature matchings. Key to the approach is that error in image feature measurement is explicitly accounted for. A Connection Machine implementation and experiments on real images are presented.
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Lee, Chen Yuan, and 李振遠. "Arm Gesture Recognition Based on Background Model." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/14546506997155104276.
Full textAbstract:
碩士國立臺灣師範大學
資訊工程研究所
99
Gesture recognition, recognize what poses a human body appears, has become an important issue in computer vision in recent. In general, gesture recognition considers different parts of human body, including head, hand and arm, and the whole body. In order to deal with gesture recognition, we need to well extract body silhouette even in a complex environment, to adopt features for gesture representation, and to design a proper classifier for recognition. In this thesis, our goal is to design a real-time presentation control system in a real classroom by recognizing the lecturer’s arm gestures only with single camera. Our proposed system is robust to strong lighting of projector and slide change in the projection screen. We first employ the mixture of Gaussian background model to segment the body silhouette of foreground. Then, the extracted feature of the body silhouette is classified as arm gestures by Support Vector Machine (SVM). In addition, the adaboosting approach of face detection helps our system to understand the left and the right hand to involve more hand actions for presentation control.
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WANG, JIA-NIAN, and 王嘉年. "Speech recognition based on neural nets model." Thesis, 1988. http://ndltd.ncl.edu.tw/handle/09784234516093226291.
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Lin, Yao-Min, and 林耀鈱. "Accelerometer-Based Trajectory Recognition via Model Identification." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/61858370469315859495.
Full textAbstract:
碩士國立臺灣海洋大學
通訊與導航工程學系
103
The application of MEMs－based smart phone device has become more and more popular in our daily life. However, due to the limits of product specifications or cost considerations, there exists a great amount of acceleration errors while executing the trajectory recognition experiment. These errors will gradually stack up and failed to rebuild the device position information, which may raise the difficulty of trajectory recognition. In this thesis, a novel trajectory recognition algorithm based on model identification is proposed. In the proposed algorithm, the acceleration data from accelerometer-based device will be analyzed for elimination measurement bias, then built the auto-regressive (AR) model for corresponding type of trajectory. We then propose the method of dynamic time wrapping (DTW) to reach the purpose of recognition by solving the best correlation between unknown testing data and existing AR models. Based on the current simulation results, the algorithm proposed in this thesis offers better performance of operation time than the existing trajectory recognition method, which usually used neural network －that need additional cost on training and learning.
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LI, HONG. "Model-Based Segmentation and Recognition of Continuous Gestures." Thesis, 2010. http://hdl.handle.net/1974/6097.
Full textAbstract:
Being one of the most active research topics in the computer vision field, automatic human gesture recognition is receiving increasing attention driven by its promising applications, ranging from surveillance and human monitoring, human-computer interface (HCI), and motion analysis, etc. Segmentation and recognition of human
dynamic gestures from continuous video streams is considered to be a highly challenging task due to the spatio-temporal variation and endpoint localization issues.
In this thesis, we propose a Motion Signature, which is a 3D spatio-temporal surface based upon the evolution of a contour over time, to reliably represent dynamic motion. A Gesture Model, is
then constructed by a set of mean and variance images of Motion Signatures in a multi-scale manner, which not only is able to
accommodate a wide range of spatio-temporal variation, but also has the advantage of requiring only a small amount of training data.
Three approaches have been proposed to simultaneously segment and recognize gestures from continuous streams, which mainly differ in the way that the endpoints of gestures are located. While the first
approach adopts an explicit multi-scale search strategy to find the endpoints of the gestures, the other two employ Dynamic Programming (DP) to handle this issue. All the three methods are rooted in the
idea that segmentation and recognition are actually the two aspects of the same problem, and that the solution to either one of them will lead to the solution of the other. This is novel to most methods in the literature, which separate segmentation and
recognition into two phases, and perform segmentation before recognition by looking into abrupt motion feature changes.
The performance of the methods has been evaluated and compared on two types of gestures: two arms movement and a single hand movement.
Experimental results have shown that all three methods achieved high recognition rates, ranging from 88% to 96% for upper body
gestures, with the last one outperforming the other two. The single hand experiment also suggested that the proposed method has the potential to be applied to the application of continuous sign language recognition.Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2010-09-24 19:27:43.316
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Wu, Chu-Mu, and 吳居穆. "Model based human motion recognition using transition diagram." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/65060677748441351506.
Full textAbstract:
碩士國立中央大學
資訊工程研究所
95
During the past decade, the technique of computer vision has been widely applied in several fields. Typical applications include virtual reality, intelligent surveillance system, human-interface, etc. There are two categories of human motion recognition approaches including model based and non-model based. Model based approach usually fits the given image or blob to a shape model, which represents joint parts and human body parts. One has to segment images into different parts, such as head, torso, arms, and legs. The drawback of this approach is that it needs more stable foreground segmentation. As to non-model based approach, it extracts features from the image, and the correspondence between consecutive frames is obtained based on estimation or prediction of features relating to shape, texture, and colors. The drawback of this kind of approach is that it is difficult to define the activity because of the lacking of pre-defined model. In this thesis, the two approaches are combined. First, we use a pre-defied model, and features are extracted from different regions in this model. In this way, the complexity of features can be reduced due to the utilization of segmented images and the system can still perform well even if the foreground image is not stable. Human motions, like walking and crawling, usually transfer smoothly in each state. Hence, a transition diagram is designed to describe the transition between different motions. Experiments were conducted and results reveal the validity of our proposed approach.
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WU, YONG-CHUAN, and 吳永川. "Mandarin syllable recognition based on simplified probability model." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/45120401017117940877.
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HONG, YI-ZHONG, and 洪一忠. "Mandarin syllable recognition based on segmental probability model." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/89993799391154192065.
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Wang, Siao-en, and 汪孝恩. "Image-based Hierarchical Model for Visual Place Recognition." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/68290528290737718647.
Full textAbstract:
碩士國立臺南大學
資訊工程學系碩士班
101
Advances in robotics-related industries make the robots, most of them are autonomous mobile robots, enter our daily lives. Map building and navigation are two fundamental abilities of autonomous mobile robot. Topological map is becoming more and more popular than metric map in recent years because topological map provides a friendly way for human to interact with mobile robots. Therefore, the place recognition technology used in topological map becomes an important issue. In this thesis, we propose a visual place recognition method which is inspired by how human perform place recognition. The proposed system consists of two different perception models to observe the environment. One applies contour orientation feature for describing the image and the other uses visual attention to find the regions of interest in the image. By using those regions of interest and order number of images for detecting the change of scene, we can cluster the sequential images taken in the similar viewing directions into one scene to drastically reduce the amount of data storage and matching time for testing phase. We hope the proposed method will be helpful to the applications of autonomous mobile robots.
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Lin, Tieng-Sheng, and 林庭陞. "Chromatic Image Recognition Based on CIELab Color Model." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/90066022432448095441.
Full textAbstract:
碩士元智大學
光電工程研究所
97
In this thesis, the CIELAB standard color vision model instead of the traditional RGB color model is utilized for polychromatic pattern recognition. The L, A and B represents the lightness, the color red-green and yellow-blue, respectively. Here, the multi-channel joint transform correlator is set to be the optical discrimination configuration. To achieve the distortion invariance in discrimination processes, we also use the minimum average correlation energy approach to yield sharp correlation peak. Besides, the image encoding technique is introduced and compared because of the cost of the device. From the numerical results, we perform the recognition compared with HSV and RGB in different channel amounts, i.e. three and two selected channels. Subsequently, the encoding technique is adopted to observe the effects on discrimination quality. We discover that the recognition results based on CIELAB model are superior to RGB generally, and case by case with HSV. So we realize that the recognition ability based on CIELAB color specification system is accepted.
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Cheng-Yuan, Chang, and 張正園. "Human Behavior Description Model based on Action Recognition." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/18122999690490915185.
Full textAbstract:
碩士中華大學
資訊工程學系(所)
97
Based on human action recognition, this paper proposes a new description model to record human behavior. Therefore, this paper mainly researches on relevant event analysis based on action interrelation; however, research relating to event analysis still stays in preliminary stage. A complete intelligent surveillance system consists of the following parts; object detecting, object tracking, action recognition, human behavior description model and event detecting, event recording, event control processing, and event prediction. The paper intends to make use of action recognition result and regard time information accumulated in action recognition as features, record human actions and time spent in these actions, then identify events through action combination and give effective processing toward these identified events. In order to prove feasibility of human behavior description model, we take events produced when pedestrians pass through cross-road as example. Under cross-road context in the experiment, total 60 films are shot when five pedestrians are passing through cross-road, producing 191 events. 187 events are correctly detected in the experiment with correct rate of 98% and error rate of 2%. On human behavior analysis, the well-defined events are able to be correctly and steadily identified and given with proper processing and control.