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检索条件"任意字段=Proceedings - 1st Canadian Conference on Computer and Robot Vision"

共 434 条记录，以下是41-50 订阅

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Detection of perceptual junctions by curve partitioning and grouping

Detection of perceptual junctions by curve partitioning and ...

引用

1st canadian conference on computer and robot vision

作者： Zheng, XF Gao, QG Dalhousie Univ Fac Comp Sci Halifax NS B3H 1W5 Canada

ISBN: (纸本)0769521274

This paper presents a perceptual organization based method for the representation and extraction of junction structures of edge segments from digital images. Perceptual Junctions (PJs) are higher-level view invariant feature entities, which are made up by intersected generic edge tokens including both linear and non-linear segments. The class of low-order PJs (LPJs) is the junctions defined by two connected segments, and detected directly by an edge tracking and partitioning algorithm. The class of high-order PJs (HPJs) is the junctions made up by more than two segments which are extended from LPJs by grouping additional segments from different edge traces. The method is robust since it mainly uses qualitative perceptual features. The computation is efficient because it is mainly involved in symbolic reasoning. The experimental results are provided.

关键词： Image analysis

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The extension of statistical face detection to face tracking

The extension of statistical face detection to face tracking

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proceedings - 1st canadian conference on computer and robot vision

作者： Wu, Haisheng Zelek, John S. Robot Vision Group School of Engineering University of Guelph N1G 2W1 Canada

ISBN: (纸本)0769521274

A real-time probabilistic face tracking system using monocular vision is presented based on face target acquisition and subsequent particle filtering techniques. First, the face target acquistion and initialization stage used a skin color classification and statistical face model matching approach to find the face target. Subsequently, the particle filtering technique is used to track the state space of face movements. And finally, the optical flow information was used to find motion information for sample redistribution. The system places emphasis on the automatic face target initialization stage, which has been assumed to be solved or labeled manually in most other face tracking systems. Using a monocular USB camera on an Intel Pentium III 700 MHz laptop, the face detection and initialization stage is executed in less than 250 msec and the subsequent face tracking stage functions at 30 fps comfortably with 160×120-pixel resolution live videos.

关键词： Face recognition

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Object tracking: Feature selection and confidence propagation

Object tracking: Feature selection and confidence propagatio...

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proceedings - 1st canadian conference on computer and robot vision

作者： Zhu, Juhua Schwartz, stuart C. Liu, Bede Dept. of Electrical Engineering Princeton University E-Quad Olden St. Princeton 08544 United States

ISBN: (纸本)0769521274

Choosing unique and invariant features is the first important step in object tracking. In this paper, we present a method to find proper-sized and irregularly-shaped trackable features, the use of which can outperform procedures using normal square features. The notion of confidence associated with each feature is introduced as the feature propagates. The use of confidence results in robust tracking even when occlusion is present. Based on the translational displacement of each feature, the affine motion of the object can be accurately estimated. This approach has been tested on a wide variety of video sequences and produces good tracking results.

关键词： Image processing

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Categorization and learning of pen motion using hidden Markov models

Categorization and learning of pen motion using hidden Marko...

引用

1st canadian conference on computer and robot vision

作者： Tausky, D Mann, R Univ Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)0769521274

In this paper we present a framework for the classification and segmentation of motion data. First, a representation of different two-dimensional motion categories is proposed. Secondly, a system to categorize and segment motion is presented based on hidden Markov models, commonly used in speech recognition. Input to the system consists of online pen stroke data which includes the x, y position and time of each point along the line. Using derived speed and direction information the system classifies and segments the input into particular categories of motion. The resulting categorical information may be then used to describe the scene, extrapolate events, or as a part of a gesture recognition system. Applications beyond pen-based input are discussed. This paper contributes to pen based motion recognition research in two ways. First, a classification is performed based on a continuous sequence of observations, rather then feature extraction. Secondly, pen motion is transformed into a translation and rotation invariant representation prior to classification.

关键词： motion recognition hidden Markov models percepts gesture recognition

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Multi-modal nonlinear feature reduction for the recognition of handwritten numerals

Multi-modal nonlinear feature reduction for the recognition ...

引用

1st canadian conference on computer and robot vision

作者： Zhang, P Suen, CY Bui, TD Concordia Univ Ctr Pattern Recognit & Machine Intelligence Montreal PQ H3G 1M8 Canada

ISBN: (纸本)0769521274

A novel method of multi-modal nonlinear feature reduction is proposed for the recognition of handwritten numerals. In order to find an effective decision boundary, each class is divided into several clusters. Then the k-NN sorting algorithm is applied to each cluster to get the training data along the effective decision boundary. Optimal discriminant analysis is implemented by multimodal nonlinear mapping to generate a between-class scatter matrix, which requires less CPU time than other nonparametric approaches. Experiments demonstrated that our proposed method could achieve a high feature reduction without sacrificing much discriminant ability. As a result, this new method can reduce ANN training complexity and make the ANN classifier more reliable. Its feature dimensionality reduction outperforms the PCA and mono-modal nonparametric analysis.

关键词： feature dimensionality reduction feature extraction handwritten character recognition discriminant analysis object recognition pattern classification

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Estimation of average cell shape digital images of cellular surfaces

Estimation of average cell shape digital images of cellular ...

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proceedings - 1st canadian conference on computer and robot vision

作者： Iles, Peter J.W. Clausi, David A. Brodland, G. Wayne University of Waterloo Waterloo Ont. N2L 3G1 Canada

ISBN: (纸本)0769521274

A method is proposed to determine the average cellular geometry in high-resolution images of embryonic epithelia. The concept of a 'composite cell' is used to represent the average cell shape. This composite cell can provide evidence of stresses present in the epithelia. A new adaptive contrast enhancement routine is applied to the input image first, followed by an iterative watershed segmentation. The composite cell is calculated from the segmentation results. Qualitative results show that this computationally inexpensive algorithm produces accurate results for a variety of image sizes, contrast levels, cell shapes and appearances.

关键词： Image analysis

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Texture analysis using Gaussian weighted grey level co-occurrence probabilities

Texture analysis using Gaussian weighted grey level co-occur...

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1st canadian conference on computer and robot vision

作者： Jobanputra, R Clausi, DA Univ Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)0769521274

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics. A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

关键词： grey level co-occurrence probabilities texture analysis image segmentation SAR sea ice texture features remote sensing imagery

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Integrating region and edge information for the automatic segmentation of interventional magnetic resonance images of the shoulder complex

Integrating region and edge information for the automatic se...

引用

1st canadian conference on computer and robot vision

作者： Tremblay, ME Albu, AB Hébert, L Laurendeau, D Univ Laval Comp Vis & Syst Lab Dept Elect Engn Ste Foy PQ G1K 7P4 Canada

ISBN: (纸本)0769521274

This paper proposes a new 2D segmentation method for MR shoulder images. Due to the significant length of the image sequences, we aim at minimizing the user intervention in the segmentation process. Our method integrates region and edge information in a coherent manner. In fact, the edge information is used in the definition of an adaptive similarity measure for iterative pixel aggregation. The seeds for the region growing process are defined automatically, which is essential for processing long image sequences with variable average brightness. Moreover, the proposed segmentation approach implements parallel region growing processes, and allows for dynamic region merging at successive iterations. To assess the performance of the proposed approach, we followed a standard methodology used for validating 2D segmentation, as well as a quantitative and qualitative evaluation of the 3D shoulder model reconstructed from the segmented image sequences.

关键词： Magnetic resonance imaging

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Shape from contours and multiple stereo -: A hierarchical, mesh-based approach

Shape from contours and multiple stereo -: A hierarchical, m...

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1st canadian conference on computer and robot vision

作者： Kück, H Heidrich, W Vogelgsang, C Univ British Columbia Dept Comp Sci Vancouver BC V6T 1W5 Canada

ISBN: (纸本)0769521274

We present a novel method for 3D shape recovery based on a combination of visual hull information and multi image stereo. We start from a coarse triangle mesh extracted from visual hull information. The mesh is then hierarchically refined and its vertex positions are optimized based on multi image stereo information. This optimization procedure utilizes 3D graphics hardware to evaluate the quality of vertex positions, and takes both color consistency, and occlusion effects as well as silhouette information into account. By directly working on a triangle mesh we are able to obtain more spatial coherence than algorithms based entirely on point information, such as voxel-based methods. This allows us to deal with objects that have very little structure in some places, as well as small specular patches.

关键词： image based modeling surface reconstruction multi view stereo triangle meshes visual hull mesh deformation hardware accelerated

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Improving CBIR systems by integrating semantic features

Improving CBIR systems by integrating semantic features

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1st canadian conference on computer and robot vision

作者： Brahmi, D Ziou, D Univ Sherbrooke Fac Sci DMI Sherbrooke PQ J1K 2R1 Canada

ISBN: (纸本)0769521274

Nowadays, access to information requires to manage effectively multimedia databases, and among challenges offered to scientific community since last decades, multimedia retrieval techniques (particularly images retrieval) are became an active research direction. Introduced to overcome the main drawbacks encountered by text-based images retrieval, which are the subjective and manual annotation of images, content based images retrieval (CBIR) systems index images according to low-level visual features such as color, texture, shape to retrieve similar images. However, despite the progress achieved in the content based image retrieval, in particular with the relevance feedback approach where the user refine the search via the specification of relevant or not relevant items, the current CBIR systems still have a major difficulty that it has yet to overcome: how to negotiate the "semantic gap"? This problem comes from the mismatch between their capabilities and the needs of users. In this paper, we address the problem of how relate low-level features to high level to bring out semantic concepts from images. Our aim is to combine content-based and metadata-based approaches for image retrieval from a user perspective to yield better results and overcome to the lacks of these techniques when they are taken separately. To represent the semantic content of images, we propose a model which takes account of the interaction between the user and the metadata. In particular, we model the semantic user' preference by analyzing its answers through the Relevance Feedback process. Furthermore, we introduce a new machine learning technique that modify the weights (i.e. relative importance) of metadata representing the semantic content of images.

关键词： Multimedia systems

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