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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

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Learning Exemplar-Based Categorization for the Detection of Multi-View Multi-Pose Objects

Learning Exemplar-Based Categorization for the Detection of ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ying Shan Feng Han H.S. Sawhney R. Kumar Sarnoff Corporation Princeton NJ USA

This paper proposes a novel approach for multi-view multi-pose object detection using discriminative shapebased exemplars. The key idea underlying this method is motivated by numerous previous observations that manually clustering multi-view multi-pose training data into different categories and then combining the separately trained two-class classifiers greatly improved the detection performance. A novel computational framework is proposed to unify different processes of categorization, training individual classifier for each intra-class category, and training a strong classifier combining the individual classifiers. The individual processes employ a single objective function that is optimized using two nested AdaBoost loops. The outer AdaBoost loop is used to select discriminative exemplars and the inner AdaBoost is used to select discriminative features on the selected exemplars. The proposed approach replaces the manual time-consuming process of exemplar selection as well as addresses the problem of labeling ambiguity inherent in this process. Also, our approach fully complies with the standard AdaBoost-based object detection framework in terms of real-time implementation. Experiments on multi-view multi-pose people and vehicle data demonstrate the efficacy of the proposed approach.

关键词： Object detection Training data Face detection Labeling Vehicle detection Clustering algorithms computer errors computer society computer vision pattern recognition

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Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours

Ultrasound-Specific Segmentation via Decorrelation and Stati...

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Slabaugh G. Unal Tong Fang M. Wels Intelligent Vision and Reasoning Department Siemens Corporate Research Inc. Princeton NJ USA Department of Computer Science Universität WÜrzburg Coburg Germany

Segmentation of ultrasound images is often a very challenging task due to speckle noise that contaminates the image. It is well known that speckle noise exhibits an asymmetric distribution as well as significant spatial correlation. Since these attributes can be difficult to model, many previous ultrasound segmentation methods oversimplify the problem by assuming that the noise is white and/or Gaussian, resulting in generic approaches that are actually more suitable to MR and X-ray segmentation than ultrasound. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that first decorrelates the image, and then performs segmentation on the whitened result using statistical region-based active contours. In particular, we design a gradient ascent flow that evolves the active contours to maximize a log likelihood functional based on the Fisher-Tippett distribution. We present experimental results that demonstrate the effectiveness of our method.

关键词： Ultrasonic imaging Decorrelation Active contours Image segmentation Speckle X-ray imaging Image quality Rayleigh scattering X-ray scattering computer science

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Shape-Based Approach to Robust Image Segmentation using Kernel PCA

Shape-Based Approach to Robust Image Segmentation using Kern...

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Dambreville Y. Rathi A. Tannen Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA

Segmentation involves separating an object from the background. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, within the level-set framework. Following the work of Leventon et al., we revisit the use of principal component analysis (PCA) to introduce prior knowledge about shapes in a more robust manner. To this end, we utilize Kernel PCA and show that this method of learning shapes outperforms linear PCA, by allowing only shapes that are close enough to the training data. In the proposed segmentation algorithm, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description allows to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, clutter, partial occlusions, or smearing.

关键词： Image segmentation Kernel Principal component analysis Shape Noise robustness computer vision Application software Active contours Training data Encoding

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Bayesian fusion of camera metadata cues in semantic scene classification

Bayesian fusion of camera metadata cues in semantic scene cl...

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conference on computer vision and pattern recognition (cvpr)

作者： M. Boutell J. Luo Department of Computer Science University of Rochester USA Research & Development Laboratories Eastman Kodak (Japan) Limited USA

Semantic scene classification based only on low-level vision cues has had limited success on unconstrained image sets. On the other hand, camera metadata related to capture conditions provides cues independent of the captured scene content that can be used to improve classification performance. We consider two problems: indoor-outdoor classification and sunset detection. Analysis of camera metadata statistics for images of each class revealed that metadata fields, such as exposure time, flash fired, and subject distance, is most discriminative for both indoor-outdoor and sunset classification. A Bayesian network is employed to fuse content-based and metadata cues in the probability domain and degrades gracefully, even when specific metadata inputs are missing (a practical concern). Finally, we provide extensive experimental results on the two problems, using content-based and metadata cues to demonstrate the efficacy of the proposed integrated scene classification scheme.

关键词： Bayesian methods Layout Digital cameras Statistical analysis Degradation Digital images Apertures computer science Laboratories Image analysis

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Novel view synthesis in tensor space

Novel view synthesis in tensor space

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conference on computer vision and pattern recognition (cvpr)

作者： S. Avidan A. Shashua Institute of Computer Science Hebrew University of Jerusalem Jerusalem Israel

We present a new method for synthesizing novel views of a 3D scene from few model images in full correspondence. The core of this work is the derivation of a tensorial operator that describes the transformation from a given tensor of three views to a novel tensor of a new configuration of three views. By repeated application of the operator on a seed tensor with a sequence of desired virtual camera positions we obtain a chain of warping functions (tensors) from the set of model images to create the desired virtual views.

关键词： Tensile stress Cameras Interpolation Layout Rendering (computer graphics) computer science Image motion analysis Engines Image generation Extrapolation

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

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

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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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Competitive mixture of deformable models for pattern classification

Competitive mixture of deformable models for pattern classif...

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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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