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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"

共 6545 条记录，以下是3951-3960 订阅

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Randomized hough transform for ellipse detection with result clustering

Randomized hough transform for ellipse detection with result...

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EUROCON 2005 - The International conference on computer as a Tool

作者： Başca, Cosmin A. Takş, Mihai Brad, Remus IEEE Romania Faculty of Engineering Lucian Blaga University of Sibiu Romania

ISBN: (纸本)142440049X

Our research is focused on the development of robust machine vision algorithms for pattern recognition. We want to provide robotic systems the ability to understand more on the external real world. In this paper we describe a method for detecting ellipses in real world images using the Randomized Hough Transform with Result Clustering. A preprocessing phase is used in which real world images are transformed - noise reduction, greyscale transform, edge detection and finaly binarization - in order to be processed by the actual ellipse detector. The ellipse detector filters out false ellipses that may interfere with the final results. Due to the fact that usually more "virtual" ellipses are detected for one "real" ellipse, a data clustering scheme is used, the clustering method, classifies all detected "virtual" ellipses into their corresponding "real" ellipses. The post processing phase is VQ similar and it also finds the actual number of classes unknown a priori. © 2005 ieee.

关键词： Data handling

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Joint nonparametric alignment for analyzing spatial gene expression patterns in Drosophila imaginal discs

Joint nonparametric alignment for analyzing spatial gene exp...

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conference on computer vision and pattern recognition (cvpr)

作者： P. Ahammad C.L. Harmon A. Hammonds S. Shankar Sastry G.M. Rubin Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Department of Molecular and Cell Biology University of California Berkeley CA USA Howard Hughes Medical Institute and Department of Molecular and Cell Biology University of California Berkeley CA USA

To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing images of corresponding tissues into alignment is a critical first step in making a meaningful comparative analysis of these spatial patterns. Significant image noise and variability in the shapes make it hard to pick a canonical shape model. In this paper, we address these problems by combining segmentation and unsupervised shape learning algorithms. We first segment images to acquire structures of interest, then jointly align the shapes of these acquired structures using an unsupervised nonparametric maximum likelihood algorithm along the lines of 'congealing' (E. G. Miller et al., 2000), while simultaneously learning the underlying shape model and associated transformations. The learned transformations are applied to corresponding images to bring them into alignment in one step. We demonstrate the results for images of various classes of Drosophila imaginal discs and discuss the methodology used for a quantitative analysis of spatial gene expression patterns.

关键词： Image analysis pattern analysis Gene expression Shape Embryo Time measurement Image segmentation Biological cells Organisms Insects

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An analysis of variational alignment of curves in images 1

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5th International conference on Scale Space and PDE Methods in computer vision, Scale-Space 2005

作者： Overgaard, Niels Chr. Solem, Jan Erik Applied Mathematics Group School of Technology and Society Malmö University Sweden

ISBN: (数字)9783540320128

ISBN: (纸本)9783540255475

In this paper a common variational formulation for alignment of curves to vector fields is analyzed. This variational approach is often used to solve the problem of aligning curves to edges in images by choosing the vector field to be the image gradient. The main contribution of this paper is an analysis of the Gâteaux derivative and the descent motion of the corresponding alignment functional, improving on earlier research in this area. Several intermediate results are proved and finally a theorem concerning necessary conditions for extremals of the alignment functional is derived. The analysis of the evolution is performed using a level set formulation and results from distribution theory. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image analysis

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Dynamic facial expression recognition using fuzzy hidden Markov models

Dynamic facial expression recognition using fuzzy hidden Mar...

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ieee Systems, Man and Cybernetics society, Proceedings - 2005 International conference on Systems, Man and Cybernetics

作者： Miners, Ben W. Basir, Otman A. Department of Electrical and Computer Engineering University of Waterloo Waterloo Ont. Canada

Humans are immersed in a high-tech computing environment. Dependence on the pervasiveness of modern devices encompasses even simple everyday tasks. Unfortunately, the rapidly increasing expectations on the intelligence of these devices often exceeds their abilities. Devices are expected to perceive their environment, understand our intent, and autonomously carry out appropriate tasks. This paper presents an approach to bring device perception one step closer to meeting the high user expectations. A novel application of fuzzy hidden Markov models to automatically identify dynamic facial expressions is proposed in a human-device interaction context. A low-complexity vision based facial feature tracker is integrated with a hidden Markov model adapted to take advantage of fuzzy measures. Benefits over traditional hidden Markov models for facial expression recognition include a reduction in training time, and improved flexibility for use as a valuable part of a larger multimodal system for natural human-device interaction. ©2005 ieee.

关键词： Face recognition

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Automatic 3D to 2D registration for the photorealistic rendering of urban scenes

Automatic 3D to 2D registration for the photorealistic rende...

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conference on computer vision and pattern recognition (cvpr)

作者： L. Liu I. Stamos Department of Computer Science Graduate Center and Hunter CollegeCity College and Graduate Center City University of New York New York NY USA

This paper presents a novel and efficient algorithm for the 3D range to 2D image registration problem in urban scene settings. Our input is a set of unregistered 3D range scans and a set of unregistered and uncalibrated 2D images of the scene. The 3D range scans and 2D images capture real scenes in extremely high detail. A new automated algorithm calibrates each 2D image and computes an optimized transformation between the 2D images and 3D range scans. This transformation is based on a match of 3D with 2D features that maximizes an overlap criterion. Our algorithm attacks the hard 3D range to 2D image registration problem in a systematic, efficient, and automatic way. Images captured by a high-resolution 2D camera, that moves and adjusts freely, are mapped on a centimeter-accurate 3D model of the scene providing photorealistic renderings of high quality. We present results from experiments in three different urban settings.

关键词： Layout Cameras Image registration Solid modeling Rendering (computer graphics) Image sensors Sensor systems computer science Educational institutions Calibration

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Facial Action Unit Detection using Probabilistic Actively Learned Support Vector Machines on Tracked Facial Point Data

Facial Action Unit Detection using Probabilistic Actively Le...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： M.F. Valstar I. Patras M. Pantic EEMCS/Mediamatics Department Delft University of Technnology Netherlands

A system that could enable fast and robust facial expression recognition would have many applications in behavioral science, medicine, security and human-machine interaction. While working toward that goal, we do not attempt to recognize prototypic facial expressions of emotions but analyze subtle changes in facial behavior by recognizing facial muscle action units (AUs, i.e., atomic facial signals) instead. By detecting AUs we can analyse many more facial communicative signals than emotional expressions alone. This paper proposes AU detection by classifying features calculated from tracked ?ducial facial points. We use a Particle Filtering tracking scheme using factorized likelihoods and a novel observation model that combines a rigid and a morphologic model. The AUs displayed in a video are classi?ed using Probabilistic Actively Learned Support VectorMachines (PAL-SVM).When tested on 167 videos from the MMI web-based facial expression database, the proposed method achieved very high recognition rates for 16 different AUs. To ascertain data independency we also performed a validation using another benchmark database. When trained on the MMI-Facial expression database and tested on the Cohn-Kanade database, the proposed method achieved a recognition rate of 84% when detecting 9 AUs occurring alone or in combination in input image sequences.

关键词： Face detection Support vector machines Face recognition Image databases Emotion recognition Signal analysis Testing Robustness Behavioral science Biomedical imaging

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Fuzzy linguistic rules classifier for wooden board color sorting

Fuzzy linguistic rules classifier for wooden board color sor...

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7th International conference on Advanced Concepts for Intelligent vision Systems

作者： Schmitt, E Bombardier, V Vogrig, R Fac Sci CRAN Automat Res Ctr Nancy UMR 7039 F-54506 Vandoeuvre Les Nancy France Luxscan Technol ZARE Ouest L-4384 Ehlerange Luxembourg

ISBN: (纸本)354029032X

This article exposes wood pieces classification method according to their color. The main difficulties encountered by the Company are primarily in the color recognition according to a certain graduality, and the decision to take on all the board with the different sides. These problems imply the use of flexible/robust model and the use of an "intelligent" information management delivered by the sensors. In order to improve the current system, we propose to integrate a method, whose principle is a fuzzy inference system, itself built thanks to fuzzy linguistic rules. The results obtained with our method show a real improvement of the recognition rate compared to a bayesian classifier already used by the Company.

关键词： pattern recognition

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Face synthesis and recognition from a single image under arbitrary unknown lighting using a spherical harmonic basis morphable model

Face synthesis and recognition from a single image under arb...

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conference on computer vision and pattern recognition (cvpr)

作者： L. Zhang S. Wang D. Samaras Computer Science Department State University of New York Stony Brook NY USA

Understanding and modifying the effects of arbitrary illumination on human faces in a realistic manner is a challenging problem both for face synthesis and recognition. Recent research demonstrates that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace using spherical harmonics representation. Morphable models are statistical ensembles of facial properties such as shape and texture. In this paper, we integrate spherical harmonics into the morphable model framework, by proposing a 3D spherical harmonic basis morphable model (SHBMM) and demonstrate that any face under arbitrary unknown lighting can be simply represented by three low-dimensional vectors: shape parameters, spherical harmonic basis parameters and illumination coefficients. We show that, with our SHBMM, given one single image under arbitrary unknown lighting, we can remove the illumination effects from the image (face "delighting") and synthesize new images under different illumination conditions (face "re-lighting"). Furthermore, we demonstrate that cast shadows can be detected and subsequently removed by using the image error between the input image and the corresponding rendered image. We also propose two illumination invariant face recognition methods based on the recovered SHBMM parameters and the de-lit images respectively. Experimental results show that using only a single image of a face under unknown lighting, we can achieve high recognition rates and generate photorealistic images of the face under a wide range of illumination conditions, including multiple sources of illumination.

关键词： Face recognition Image recognition Lighting Humans Face detection Image generation Shape Rendering (computer graphics) Image databases computer science

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Human Learning of Contextual Priors for Object Search: Where does the time go?

Human Learning of Contextual Priors for Object Search: Where...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： B. Hidalgo-Sotelo A. Oliva A. Torralba Department of Brain and Cognitive Sciences MIT Cambridge MA USA Computer Science and Artificial Intelligence Laboratory MIT Cambridge MA USA

Attention allocation in visual search is known to be influenced by low-level image features, visual scene context and top down task constraints. Here, we investigate the role of Contextual priors in guiding visual search by monitoring eye movements as participants search very familiar scenes for a target object. The goal of the study is to identify which stage of the visual search benefits from contextual priors. Two groups of participants differed in the expectation of target presence associated with a scene. Stronger priors are established when a scene exemplar is always associated with the presence of the target than when the scene is periodically observed with and without the target. In both cases, overall search performance improves over repeated presentations of scenes. An analytic decomposition of the time course of the effect of contextual priors shows a time benefit to the exploration stage of search (scan time) and a decrease in gaze duration on the target. The strength of the contextual relationship modulates the magnitude of gaze duration gain, while the scan time gain constitutes one half of the overall search performance benefit regardless of the probability (50% or 100%) of target presence. These data are discussed in terms of the implications of contextdependent scene processing and its putative role in various stages of visual search.

关键词： Humans Layout Performance gain Learning computer science Artificial intelligence Laboratories computerized monitoring Cellular phones Lamps

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Unsupervised Segmentation of Multi-Modal Images by a Precise Approximation of Individual Modes with Linear Combinations of Discrete Gaussians

Unsupervised Segmentation of Multi-Modal Images by a Precise...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： A. El-Baz R.M. Mohamed A.A. Farag G. Gimel'farb CVIP Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Department of Computer Science Tamaki Campus University of Auckland Auckland New Zealand

Many important applications of image analysis deal with multi-modal images such that each object of interest relates to an individual mode in the marginal signal distribution collected over the image. Segmentation of such seemingly simple images is nonetheless a challenging problem because each meaningful boundary between the objects is rarely formed by easily detectable signal differences (or "edges"). Most commonly, the signals have very close values across the boundary and relate to intersecting tails of distributions describing individual objects. To accurately segment such images, not only the main body but also the tails of each such distribution have to be precisely recovered from the available mixture. We present a *** version of our novel EM-based algorithm for accurate unsupervised segmentation of multi-modal grayscale images. It has a considerably improved convergence to a local maximum of the image likelihood and provides a very close approximation of each distribution related to the mode with a linear combination of sign-alternate discrete Gaussian kernels. Experiments with medical images show the proposed segmentation is more accurate than several other known alternatives.

关键词： Image segmentation Gaussian approximation Image edge detection Probability distribution Object detection Signal detection Gray-scale Convergence Gaussian distribution Kernel

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