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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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Visual recognition using concurrent and layered parameter networks

Visual recognition using concurrent and layered parameter ne...

引用

conference on computer vision and pattern recognition (cvpr)

作者： R.M. Bolle A. Califano R. Kjeldsen R.W. Taylor IBM Thomas J. Watson Research Center Exploratory Computer Vision Group Yorktown Heights NY USA

A vision system to recognize 3-D objects is presented. A novel notion of generalized feature makes it possible to develop a homogeneous architecture to support recognition from simple partial features to complex feature assemblies and 3-D objects. Layered concurrent parameter transforms vote for feature hypotheses on the basis of image data and previously reconstructed features. recognition networks, motivated by connectionist systems, collect votes, fuse evidence from various sources and ensure global consistency. In addition, they provide an integrated solution to the segmentation problem. The highly modular design allows fundamentally different types of features to interact in a coherent way, utilizing redundancies for more robust recognition. Within this paradigm, a system extracts planar patches, patches of quadrics of revolution, and the intersection curves of these surfaces (lines and conic sections in three-space) from a depth map. Reconstructed features index into a model database to form consistent object hypotheses. Experimental results detailing the recognition behavior or real depth maps are included.< >

关键词： Voting Image reconstruction Machine vision Assembly Fuses Image segmentation Robustness Surface reconstruction Indexes Spatial databases

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A Multichannel Edge-Weighted Centroidal Voronoi Tessellation algorithm for 3D super-alloy image segmentation

A Multichannel Edge-Weighted Centroidal Voronoi Tessellation...

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作者： Cao, Yu Ju, Lili Zou, Qin Qu, Chengzhang Wang, Song Department of Computer Science and Engineering University of South Carolina Columbia SC 29208 United States Department of Mathematics University of South Carolina Columbia SC 29208 United States School of Remote Sensing and Infomation Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9781457703942

In material science and engineering, the grain structure inside a super-alloy sample determines its mechanical and physical properties. In this paper, we develop a new Multichannel Edge-Weighted Centroidal Voronoi Tessellation (MCEWCVT) algorithm to automatically segment all the 3D grains from microscopic images of a super-alloy sample. Built upon the classical k-means/CVT algorithm, the proposed algorithm considers both the voxel-intensity similarity within each cluster and the compactness of each cluster. In addition, the same slice of a super-alloy sample can produce multiple images with different grain appearances using different settings of the microscope. We call this multichannel imaging and in this paper, we further adapt the proposed segmentation algorithm to handle such multichannel images to achieve higher grain-segmentation accuracy. We test the proposed MCEWCVT algorithm on a 4-channel Ni-based 3D super-alloy image consisting of 170 slices. The segmentation performance is evaluated against the manually annotated ground-truth segmentation and quantitatively compared with other six image segmentation/edge-detection methods. The experimental results demonstrate the higher accuracy of the proposed algorithm than the comparison methods. © 2011 ieee.

关键词： Image segmentation

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Unsupervised Learning of Categories from Sets of Partially Matching Image Features

Unsupervised Learning of Categories from Sets of Partially M...

引用

conference on computer vision and pattern recognition (cvpr)

作者： K. Grauman T. Darrell Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into a space where they cluster according to their partial-match feature correspondences. After efficiently computing the pairwise affinities between the input images in this space, a spectral clustering technique is used to recover the primary groupings among the images. We introduce an efficient means of refining these groupings according to intra-cluster statistics over the subsets of features selected by the partial matches between the images, and based on an optional, variable amount of user supervision. We compute the consistent subsets of feature correspondences within a grouping to infer category feature masks. The output of the algorithm is a partition of the data into a set of learned categories, and a set of classifiers trained from these ranked partitions that can recognize the categories in novel images.

关键词： Unsupervised learning Prototypes Space technology computer science Artificial intelligence Laboratories Statistics Partitioning algorithms Image recognition Layout

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Multiple cardiac arrhythmia recognition using adaptive wavelet network

Multiple cardiac arrhythmia recognition using adaptive wavel...

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2005 27th Annual International conference of the Engineering in Medicine and Biology society, ieee-EMBS 2005

作者： Lin, Chia-Hung Chen, Pei-Jarn Chen, Yung-Fu Lee, You-Yun Chen, Tainsong Department of Electrical Engineering Kao-Yuan Institute of Technology Kaohsiung 821 Taiwan Institute of Biomedical Engineering National Cheng-Kung University Tainan 701 Taiwan Department of Computer Science and Information Engineering Dayeh University Changhua 515 Taiwan Department of Electrical Engineering Southern Taiwan University of Technology Tainan Conty 710 Taiwan

ISBN: (纸本)0780387406

This paper proposes a method for electrocardiogram (ECG) heartbeat pattern recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting from feature extraction and conversion of QRS complexes, and then identifying cardiac arrhythmias based on the detected features. The discrimination method of ECG beats is a two-subnetwork architecture, consisting of a wavelet layer and a probabilistic neural network (PNN), Morlet wavelets are used to extract the features from each heartbeat, and then PNN is used to analyze the meaningful features and perform discrimination tasks. The AWN is suitable for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results obtained by testing the data of the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed method. © 2005 ieee.

关键词： Electrocardiography

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Estimation of motion parameters for a deformable object from range data

Estimation of motion parameters for a deformable object from...

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conference on computer vision and pattern recognition (cvpr)

作者： S. Chaudhuri S. Chatterjee Department of Electrical & Computer Engineering University of California San Diego CA USA

If the correspondence between two sets of points representing the coordinates of different points of an object undergoing rotational motion and deformation is known, the parameters can be estimated using different least-squares estimators. The total-least-squares (TLS) method is very appropriate when the observation and the data matrices are both perturbed by random noise. For Gaussian-distributed noise, the TLS solution is equivalent to maximum-likelihood estimation. The mean-square error in TLS is always smaller than in an ordinary least-squares (LS) estimator. The scope is analyzed of TLS in estimating the generalized motion parameters, as is the feasibility of decomposing the generalized motion parameters in terms of rotation and deformation parameters. The performance of TLS is compared to that of the LS estimator.< >

关键词： Motion estimation Parameter estimation Least squares approximation Matrix decomposition Least squares methods Maximum likelihood estimation Data engineering Gaussian noise Mean square error methods Motion analysis

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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 ...

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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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Shape from Shading: Recognizing the Mountains through a Global View

Shape from Shading: Recognizing the Mountains through a Glob...

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conference on computer vision and pattern recognition (cvpr)

作者： Qihui Zhu Jianbo Shi Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

Resolving local ambiguities is an important issue for shape from shading (SFS). Pixel ambiguities of SFS can be eliminated by propagation approaches. However, patch ambiguities still exist. Therefore, we formulate the global disambiguation problem to resolve these ambiguities. Intuitively, it can be interpreted as flipping patches and adjusting heights such that the result surface has no kinks. The problem is intractable because exponentially many possible configurations need to be checked. Alternatively, we solve the integrability testing problems closely related to the original one. It can be viewed as finding a surface which satisfies the global integrability constraint. To encode the constraints, we introduce a graph formulation called configuration graph. Searching the solution on this graph can be reduced to a Max-cut problem and its solution is computuble using semidefinite programming (SDP) relaxation. Tests carried out on synthetic and real images show that the global disambiguation works well for complex shapes.

关键词： Shape Surface reconstruction Image reconstruction Equations Information science Testing Pixel Lighting Venus computer society

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Fast segmentation of range images into planar regions

Fast segmentation of range images into planar regions

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conference on computer vision and pattern recognition (cvpr)

作者： F. Schmitt X. Chen École Nationale Supérieure des Télécommunications Paris France

A split-and-merge algorithm is proposed for the segmentation of the digitized surface of a range image into planar regions. The geometric data structure used is a triangular tessellation of image domain. This data structure, combined with an adaptive surface approximation technique, allows a better adaptation of the range image segmentation to the surface boundaries. It also provides an efficient neighborhood referencing mechanism, thus resulting in a fast algorithm.< >

关键词： Image segmentation Data structures Merging Approximation algorithms Approximation error Computational efficiency computer vision Application software computer errors Q measurement

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Hidden Markov Model based 2D shape classification

Hidden Markov Model based 2D shape classification

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7th International conference on Advanced Concepts for Intelligent vision Systems

作者： Thakoor, N Gao, J Univ Texas Arlington TX 76013 USA Univ Texas Arlington TX 76013 USA

ISBN: (纸本)354029032X

In this paper, we propose a novel two step shape classification approach consisting of a description and a discrimination phase. In the description phase, curvature features are extracted from the shape and are utilized to build a Hidden Markov Model (HMM). The HMM provides a robust Maximum Likelihood (ML) description of the shape. In the discrimination phase, a weighted likelihood discriminant function is formulated, which weights the likelihoods of curvature at individual points of shape to minimize the classification error. The weighting scheme emulates feature selection procedure in which features important for classification are selected. A Generalized Probabilistic Descent (GPD) method based method for estimation of the weights is proposed. To demonstrate the accuracy of the proposed method, we present classification results achieved for fighter planes in terms of classification accuracy and discriminant functions.

关键词： pattern recognition

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Image classification by non-negative sparse coding, low-rank and sparse decomposition

Image classification by non-negative sparse coding, low-rank...

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作者： Zhang, Chunjie Liu, Jing Tian, Qi Xu, Changsheng Lu, Hanqing Ma, Songde National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences P.O.Box 2728 Beijing China University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 United States

ISBN: (纸本)9781457703942

We propose an image classification framework by leveraging the non-negative sparse coding, low-rank and sparse matrix decomposition techniques (LR-ScSPM). First, we propose a new non-negative sparse coding along with max pooling and spatial pyramid matching method (Sc+ SPM) to extract local features' information in order to represent images, where non-negative sparse coding is used to encode local features. Max pooling along with spatial pyramid matching (SPM) is then utilized to get the feature vectors to represent images. Second, motivated by the observation that images of the same class often contain correlated (or common) items and specific (or noisy) items, we propose to leverage the low-rank and sparse matrix recovery technique to decompose the feature vectors of images per class into a low-rank matrix and a sparse error matrix. To incorporate the common and specific attributes into the image representation, we still adopt the idea of sparse coding to recode the Sc + SPM representation of each image. In particular, we collect the columns of the both matrixes as the bases and use the coding parameters as the updated image representation by learning them through the locality-constrained linear coding (LLC). Finally, linear SVM classifier is leveraged for the final classification. Experimental results show that the proposed method achieves or outperforms the state-of-the-art results on several benchmarks. © 2011 ieee.

关键词： Image representation

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