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共 467 条记录，以下是91-100 订阅

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Multi-modal Scene Reconstruction using Perceptual Grouping Constraints

Multi-modal Scene Reconstruction using Perceptual Grouping C...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： N. Pugeault F. Worgotter N. Kruger University of Edinburgh UK University of Göttingen Germany Aalborg University Copenhagen Denmark

In this work we propose a scheme integrating perceptual grouping into stereopsis to reduce the ambiguity of those early processes. We propose a simple perceptual grouping algorithm that - in addition to the geometric information makes use of a novel multi-modal affinity measure between local primitives. We then use this group information to 1) disambiguate the stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. We show quantitative and qualitative demonstrations of those processes on a variety of sequences.

关键词： Layout Image reconstruction Stereo vision Data mining Stereo image processing Interpolation Fluid flow measurement Phase measurement Testing Error correction

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Robust Statistical Estimation and Segmentation of Multiple Subspaces

Robust Statistical Estimation and Segmentation of Multiple S...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： A.Y. Yang S.R. Rao Yi Ma Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana-Champaign IL USA

We study the problem of estimating a mixed geometric model of multiple subspaces in the presence of a significant amount of outliers. The estimation of multiple subspaces is an important problem in computer vision, particularly for segmenting multiple motions in an image sequence. We first provide a comprehensive survey of robust statistical techniques in the literature, and identify three main approaches for detecting and rejecting outliers. Through a careful examination of these approaches, we propose and investigate three principled methods for robustly estimating mixed subspace models: random sample consensus, the influence function, and multivariate trimming. Using a benchmark synthetic experiment and a set of real image sequences, we conduct a thorough comparison of the three methods

关键词： Robustness Motion estimation computer vision Image segmentation Layout Image sequences Solid modeling Geometry Face detection Cameras

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Segmentation of Neighboring Structures by Modeling Their Interaction

Segmentation of Neighboring Structures by Modeling Their Int...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Pingkun Yan Mubarak Shah School of Computer Science University of Central Florida USA

The similarity of pixel intensities, presence of noise, existence of partial volume effect and so on make segmentation of neighboring structures in medical image a challenging task. In this paper, we present a novel approach for segmenting multiple neighboring organs simultaneously by modeling interaction between them. Our method is motivated by the observation that radiologists mark ambiguous boundaries by considering all surrounding anatomic structures. In the proposed interaction model, the connectedness, competition and repulsion between organs are analyzed. By quantitatively defining these interaction components, an energy functional is formulated and structures are obtained by minimizing the energy. An energy minimization algorithm based on multiway graph cuts is proposed, which obtains an approximation within a known factor of the global minimum. The promising experimental results on various medical images demonstrate the performance of our segmentation algorithm.

关键词： Image segmentation Deformable models Biomedical imaging Level set Minimization methods Anatomical structure computer vision Topology computer science Pixel

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Finding Minimal Parameterizations of Cylindrical Image Manifolds

Finding Minimal Parameterizations of Cylindrical Image Manif...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： M. Dixon N. Jacobs R. Pless Department of Computer Science and Engineering Washington University Saint Louis MO USA

Manifold learning has become an important tool to characterize high-dimensional data that vary nonlinearly due to a few parameters. Applications to the analysis of medical imagery and human motion patterns have been successful despite the lack of effective tools to parameterize cyclic data sets. This paper offers an initial approach to this problem, and provides for a minimal parameterization of points that are drawn from cylindrical manifolds-data whose (unknown) generative model includes a cyclic and a non-cyclic parameter. Solving for this special case is important for a number of current, practical applications and provides a start toward a general approach to cyclic manifolds. We offer results on synthetic and real data sets and illustrate an application to de-noising cardiac ultrasound images.

关键词： Topology Manifolds Jacobian matrices Noise reduction Ultrasonic imaging computer science Biomedical engineering Data engineering Application software Image analysis

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Perceptual Information of Images and the Bias in Homogeneity-based Segmentation

Perceptual Information of Images and the Bias in Homogeneity...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hui Zhang S.A. Goldman Department of Computer Science and Engineering Washington University Saint Louis MO USA

Semantic image segmentation aims to partition an image into separate regions, which ideally corresponds to different real-world objects. Many segmentation algorithms have been proposed, exploiting a wide variety of image features and characteristics. It has been shown through empirical studies that segmentation methods that assume a good segmentation partitions an image into different homogeneous regions are likely to fail in non-trivial situations, while methods based on perceptual organization generally generate more favorable segmentations. Yet no formal justification has been provided. In this paper, we propose an information measure for images, the perceptual information, based on human visual perception organization. Using perceptual information, we justify that homogeneity-based segmentation methods are inherently biased, and by incorporating knowledge, perceptual organization can overcome the bias and generate better segmentations.

关键词： Image segmentation Humans Visual perception Partitioning algorithms computer vision computer science Drives Application software Image analysis Object recognition

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ImprovingWeb-based Image Search via Content Based Clustering

ImprovingWeb-based Image Search via Content Based Clustering

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： N. Ben-Haim B. Babenko S. Belongie Department of Computer Science and Engineering University of California San Diego USA

Current image search engines on the web rely purely on the keywords around the images and the filenames, which produces a lot of garbage in the search results. Alternatively, there exist methods for content based image retrieval that require a user to submit a query image, and return images that are similar in content. We propose a novel approach named ReSPEC (Re-ranking Sets of Pictures by Exploiting Consistency), that is a hybrid of the two methods. Our algorithm first retrieves the results of a keyword query from an existing image search engine, clusters the results based on extracted image features, and returns the cluster that is inferred to be the most relevant to the search query. Furthermore, it ranks the remaining results in order of relevance.

关键词： Search engines Image retrieval Clustering algorithms Content based retrieval Feature extraction Object recognition computer science Joining processes Region 2 Humans

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Kernel-based recognition of Human Actions Using Spatiotemporal Salient Points

Kernel-based Recognition of Human Actions Using Spatiotempor...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： A. Oikonomopoulos I. Patras M. Pantic Computing Department Imperial College London UK Computer Vision and Pattern Recognition Group University of York UK

This paper addresses the problem of human action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. We detect the spatiotemporal salient points by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. We derive a suitable distance measure between the representations, which is based on the Chamfer distance, and we optimize this measure with respect to a number of temporal and scaling parameters. In this way we achieve invariance against scaling, while at the same time, we eliminate the temporal differences between the representations. We use Relevance Vector Machines (RVM) in order to address the classification problem. We propose new kernels for use by the RVM, which are specifically tailored to the proposed spatiotemporal salient point representation. The basis of these kernels is the optimized Chamfer distance of the previous step. We present results on real image sequences from a small database depicting people performing 19 aerobic exercises.

关键词： Humans Spatiotemporal phenomena pattern recognition computer vision Image recognition Image sequences Detectors Entropy Image retrieval Educational institutions

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Belief Propagation Driven Method for Facial Gestures recognition in Presence of Occlusions

Belief Propagation Driven Method for Facial Gestures Recogni...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wei-Kai Liao I. Cohen Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

In this paper, we present a framework for automatic facial gestures recognition in a highly interactive environment. We propose to use a region-based representation of the face, and model the interdependencies of these regions using a graphical model. A parametric modeling of the local facial deformations and the use of a graphical model facilitate the characterization of intra-region dynamic patterns and the inter-region dependencies. We augment this formalism with a belief propagation algorithm to infer missing data, as well as correcting for erroneous estimations of the local deformations. The resulting approach hence handles complete observation and partial occlusion cases in an unified way, and allows for the recognition of facial gestures in presence of head motion and partial occlusions of the face. Experimental results on recognizing facial expression in various real world situations, such as non-frontal views, moving head, and occlusions illustrate the proposed approach.

关键词： Belief propagation Face recognition Graphical models Face detection Intelligent robots Parametric statistics computer vision Robotics and automation Intelligent systems Emotion recognition

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A Proposed Pipelined-Architecture for FPGA-Based Affine-Invariant Feature Detectors

A Proposed Pipelined-Architecture for FPGA-Based Affine-Inva...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： C. Cabani W.J. MacLean Department of Electrical & Computer Engineering University of Toronto Toronto ONT Canada

This paper describes a hardware architecture for an FPGAbased implementation of affine-invariant image feature detectors, following the algorithm of Mikolajczyk & Schmid. The architecture mimics the structure of the algorithm by implementing a multi-scale Harris corner detector which feeds candidate points into an iterative procedure to determine the local affine shape of the feature’s neighbourhood (up to an undetermined rotation). Since the algorithm is iterative, and since we desire a high throughput rate, the iterations are "unrolled" into a sequence of identical computation blocks arranged in a pipeline architecture. The modularity of the resulting architecture allows for scaling the implementation to devices of different resource capacity, as well as partitioning the algorithm over several devices. The final implementation, when completed, will be part of a smart-camera system which outputs features at the same time as the associated images.

关键词： computer vision Iterative algorithms Detectors Partitioning algorithms computer architecture Hardware Feeds Shape Throughput Pipelines

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Beyond RANSAC: User Independent Robust Regression

Beyond RANSAC: User Independent Robust Regression

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： R. Subbarao P. Meer Department of Electrical and Computer Engineering Rutgers University Piscataway NJ USA

RANSAC is the most widely used robust regression algorithm in computer vision. However, RANSAC has a few drawbacks which make it difficult to use in a lot of applications. Some of these problems have been addressed through improved sampling algorithms or better cost functions, but an important problem still remains. The algorithms are not user independent, and require some knowledge of the scale of the inlier noise. The projection based M-estimator (pbM) offers a solution to this by reframing the regression problem in a projection pursuit framework. In this paper we derive the pbM algorithm for heteroscedastic data. Our algorithm is applied to various real problems and its performance is compared with RANSAC and MSAC. It is shown that pbM gives better results than RANSAC and MSAC in spite of being user independent.

关键词： computer vision Application software Sampling methods Motion estimation Parameter estimation Cost function Interference Noise robustness Project management Kernel

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