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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是4941-4950 订阅

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Chaotic Associative Recalls for Fixed Point Attractor patterns

Chaotic Associative Recalls for Fixed Point Attractor Patter...

引用

International Joint conference on Neural Networks 2003

作者： Zhao, Liang Cáceres, Juan C.G. Szu, Harold Inst. of Math. and Computer Science University of São Paulo Sao Paulo Brazil Digital Media RF Lab. Sch. of Eng. and Applied Science George Washington University Washington DC United States

Human perception is a complex nonlinear dynamics. On the one hand it is periodic dynamics and on the other hand it is chaotic. Thus, we wish to propose a hybrid - the spatial chaotic dynamics for the associative recall to retrieve patterns, similar to Walter Freeman's discovery, and the fixed point dynamics for memory storage, similar to Hopfield and Grossberg's discoveries. In this model, each neuron in the network could be a chaotic map, whose phase space is divided into two states: one is periodic dynamic state with period-V, which is used to represent a V-value retrieved pattern;another is chaotic dynamic state. Firstly, patterns are stored in the memory by fixed point learning algorithm. In the retrieving process, all neurons are initially set in the chaotic region. Due to the ergodicity property of chaos, each neuron will approximate the periodic points covered by the chaotic attractor at same instants. When this occurs, the control is activated to drive the dynamic of each neuron to their corresponding stable periodic point. computer simulations confirm the theoretical prediction.

关键词： pattern recognition

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Articulated object recognition, or: how to generalize the generalized Hough transform

Articulated object recognition, or: how to generalize the ge...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Beinglass H.J. Wolfson Computer Science Department Sackler Faculty of Exact Sciences Tel-Aviv University Tel-Aviv Israel

A method for model-based recognition of articulated objects in cluttered scenes is presented. The objects consist of rigid parts connected by rotary or prismatic joints. The method is based on an extension of the generalized Hough transform approach. It is applicable to various viewing transformations. Unlike previous methods there is no significant degradation in performance for recognition of articulated objects compared with the recognition of rigid objects containing similar amounts of information. The method was implemented and successfully tested for recognition of partially overlapping 2-D objects with rotary joints which have undergone rotation, translation, and scaling.< >

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Using real shadows to create virtual ones

引用

13th Scandinavian conference on Image Analysis, SCIA 2003

作者： Madsen, Claus B. Laboratory of Computer Vision and Image Analysis Aalborg University Aalborg EastDK-9220 Denmark

ISBN: (纸本)3540406018

In Augmented Reality (AR) real imagery is superimposed by computer graphics renderings of virtual objects. This paper addresses the problem of creating the illusion that the virtual objects cast credible shadows in the real scenario. A step towards this is to be able to detect shadow regions in images of natural scenes, and this paper describes an approach to such shadow detection. The shadow detection can distinguish between directly lit areas, penumbra (half-shadow) areas, and umbra (full shadow) areas. Furthermore, it is demonstrated how virtual shadows can be created which appear very similar to the real shadows. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Augmented reality

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Competitive mixture of deformable models for pattern classification

Competitive mixture of deformable models for pattern classif...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Kwok-Wai Cheung Dit-Yan Yeung R.T. Chin Department of Computler Science Hong Kong University of Science and Technology Hong Kong China Hong Kong University of Science and Technology Kowloon HK Dept. of Comput. Sci. Hong Kong Univ. of Sci. & Technol. Clear Water Bay Hong Kong

Following the success of applying deformable models to feature extraction, a natural next step is to apply such models to pattern classification. Recently, we have cast a deformable model under a Bayesian framework for classification, giving promising results. However, deformable model methods are computationally expensive due to the required iterative optimization process. The problem is even more severe when there are a large number of models (e.g., for character recognition), because each of them has to deform and match with the input data before a final classification can be derived. In this paper, we propose to combine the deformable models into a mixture, in which the individual models compete with each other to survive the matching process during classification. Models that do not compete well are eliminated early, thus allowing substantial savings in computation. This process of competition-elimination has been applied to handwritten digit recognition in which significant speedup can be achieved without sacrificing recognition accuracy.

关键词： Deformable models pattern classification Bayesian methods Handwriting recognition Feature extraction Image recognition Delta modulation pattern recognition Context modeling Iterative methods

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Active Graph Cuts

Active Graph Cuts

引用

conference on computer vision and pattern recognition (cvpr)

作者： O. Juan Y. Boykov CERTIS Ecole Nationale des Ponts et Chaussees Champs sur Marne France Computer Science Department University of Western Ontario London ONT Canada

This paper adds a number of novel concepts into global s/t cut methods improving their efficiency and making them relevant for a wider class of applications in vision where algorithms should ideally run in real-time. Our new Active Cuts (AC) method can effectively use a good approximate solution (initial cut) that is often available in dynamic, hierarchical, and multi-label optimization problems in vision. In many problems AC works faster than the state-of-the-art max-flow methods [2] even if initial cut is far from the optimal one. Moreover, empirical speed improves several folds when initial cut is spatially close to the optima. Before converging to a global minima, Active Cuts outputs a multitude of intermediate solutions (intermediate cuts) that, for example, can be used be accelerate iterative learning-based methods or to improve visual perception of graph cuts realtime performance when large volumetric data is segmented. Finally, it can also be combined with many previous methods for accelerating graph cuts.

关键词： Application software Acceleration Visual perception Costs Image segmentation computer vision Testing computer science Optimization methods Iterative methods

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Multidimensional indexing for recognizing visual shapes

Multidimensional indexing for recognizing visual shapes

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conference on computer vision and pattern recognition (cvpr)

作者： A. Califano R. Mohan Exploratory Computer Vision Group IBM Thomas J. Watson Research Center Yorktown Heights NY USA

A homogeneous approach for acquisition, storage, and recognition of nonparametric shapes from images, using a novel shape representation based on shape autocorrelation operators is presented. A theoretical and experimental analysis of the computational complexity, recognition performance with increasing database size, and fault tolerance of the approach is presented. The system has been tested extensively with more than 300 arbitrary shapes in the database. Using a set of complex shapes, the recognition behavior with respect to occlusion, geometric transformation, and cluttered environments is studied. Unsupervised shape and subpart acquisition is demonstrated.< >

关键词： Multidimensional systems Indexing Shape Image storage Image databases Spatial databases Image recognition Autocorrelation Performance analysis Computational complexity

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Off-line handwritten word recognition (HWR) using a single contextual hidden Markov model

Off-line handwritten word recognition (HWR) using a single c...

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conference on computer vision and pattern recognition (cvpr)

作者： M.-Y. Chen A. Kundu J. Zhou Center of Excellence for Document Analysis and Recognition Suny at Buffalo Buffalo NY USA

A complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model (HMM) is proposed. The scheme includes a morphology- and heuristics-based segmentation algorithm and a modified Viterbi algorithm that searches the (l+1)st globally best path based on the previous l best paths. The results of detailed experiments for which the overall recognition rate is up to 89.4% are reported.< >

关键词： Handwriting recognition Hidden Markov models Image segmentation Speech recognition Speech processing Postal services Text analysis Image recognition

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A Smart Sensor with Hyper spectral/Range Fovea and Panoramic Peripheral View

A Smart Sensor with Hyper spectral/Range Fovea and Panoramic...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Tao Wang Zhigang Zhu Harvey Rhody Department of Computer Science Department of Computer Science The CUNY Graduate Center Center for Imaging Science Rochester Institute of Technology

ISBN: (纸本)9781424439942

We propose an adaptive and effective multimodal peripheral-fovea sensor design for real-time targets tracking. This design is inspired by the biological vision systems for achieving real-time target detection and recognition with a hyperspectral/range fovea and panoramic peripheral view. A realistic scene simulation approach is used to evaluate our sensor design and the related data exploitation algorithms before a real sensor is made. The goal is to reduce development time and system cost while achieving optimal results through an iterative process that incorporates simulation, sensing, processing and evaluation. Important issues such as multimodal sensory component integration, region of interest extraction, target tracking, hyperspectral image analysis and target signature identification are discussed.

关键词： Hyperspectral imaging Panoramic vision bio-inspired sensing Target tracking

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Online learning of probabilistic appearance manifolds for video-based recognition and tracking

Online learning of probabilistic appearance manifolds for vi...

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conference on computer vision and pattern recognition (cvpr)

作者： Kuang-Chih Lee D. Kriegman Urbana-Champaign University of Illinois Urbana-Champaign IL USA Computer Science & Engineering University of California San Diego La Jolla CA USA

This paper presents an online learning algorithm to construct from video sequences an image-based representation that is useful for recognition and tracking. For a class of objects (e.g., human faces), a generic representation of the appearances of the class is learned off-line. From video of an instance of this class (e.g., a particular person), an appearance model is incrementally learned on-line using the prior generic model and successive frames from the video. More specifically, both the generic and individual appearances are represented as an appearance manifold that is approximated by a collection of sub-manifolds (named pose manifolds) and the connectivity between them. In turn, each sub-manifold is approximated by a low-dimensional linear sub-space while the connectivity is modeled by transition probabilities between pairs of sub-manifolds. We demonstrate that our online learning algorithm constructs an effective representation for face tracking, and its use in video-based face recognition compares favorably to the representation constructed with a batch technique.

关键词： Video sequences Principal component analysis Image recognition computer science Machine learning Machine learning algorithms Face recognition Streaming media Manifolds Humans

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SUSurE: Speeded Up Surround Extrema Feature Detector and Descriptor for Real-time Applications

SUSurE: Speeded Up Surround Extrema Feature Detector and Des...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Mosalam Ebrahimi Walterio W. Mayol-Cuevas Department of Computer Science University of Bristol Bristol United Kingdom

ISBN: (纸本)9781424439942

There has been significant research into the development of visual feature detectors and descriptors that are robust to a number of image deformations. Some of these methods have emphasized the need to improve on computational speed and compact representations so that they can enable a range of real-time applications with reduced computational requirements. In this paper we present modified detectors and descriptors based on the recently introduced CenSurE [l], and show experimental results that aim to highlight the computational savings that can be made with limited reduction in performance. The developed methods are based on exploiting the concept of sparse sampling which may be of interest to a range of other existing approaches.

关键词： computer vision Detectors Filters Robustness Information services Web sites Internet Application software Kernel Image databases

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