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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是1511-1520 订阅

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theory for variational area-based segmentation using non-quadratic penalty functions

Theory for variational area-based segmentation using non-qua...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Karlsson N.C. Overgaard Applied Mathematics Group School of Technology and Society Malmö University Sweden

In this paper a theory is developed for variational segmentation of images using area-based segmentation functionals with non-quadratic penalty functions in the fidelity term. Two small theorems, which we believe are new to the vision community, allow us to compute the Gateaux derivative of the considered functional, and to construct the corresponding gradient descent flow. the functional is minimized by evolving an initial curve using this gradient descent flow. If the penalty function is sub-quadratic, i.e. behaves like the p'th power of the error for p<2, the obtained segmentation model is more robust with respect to noise and outliers than the classical Chan-Vese model and the curve evolution has better convergence properties.

关键词： Image segmentation Active contours Mathematics computer vision Noise robustness Active noise reduction Convergence Object detection Length measurement Contracts

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Boosting saliency in color image features

Boosting saliency in color image features

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conference on computer vision and pattern recognition (cvpr)

作者： J. van de Weijer th. Gevers Intelligent Sensory Information Systems Faculty of Science University of Amsterdam Amsterdam Netherlands

the aim of salient point detection is to find distinctive events in images. Salient features are generally determined from the local differential structure of images. they focus on the shape saliency of the local neighborhood. the majority of these detectors is luminance based which has the disadvantage that the distinctiveness of the local color information is completely ignored. To fully exploit the possibilities of color image salient point detection, color distinctiveness should be taken into account next to shape distinctiveness. In this paper color distinctiveness is explicitly incorporated into the design of saliency detection. the algorithm, called color saliency boosting, is based on an analysis of the statistics of color image derivatives. Isosalient color derivatives can be closely approximated by ellipsoidal surfaces in color derivative space. Based on this remarkable statistical finding, isosalient derivatives are transformed by color boosting to have equal impact on the saliency. Color saliency boosting is designed as a generic method easily adaptable to existing feature detectors. Results show that substantial improvements in information content are acquired by targeting color salient features. Further, the generality of the method is illustrated by applying color boosting to multiple existing saliency methods.

关键词： Boosting Detectors computer vision Phase detection Shape Data mining Image color analysis Intelligent systems Intelligent sensors Information systems

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thermal face recognition in an operational Scenario

Thermal face recognition in an operational Scenario

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Proceedings of the 2004 ieee computer Society conference on computer vision and pattern recognition, cvpr 2004

作者： Socolinsky, Diego A. Selinger, Andrea Equinox Corporation 207 East Redwood St. Baltimore MD 21202 Equinox Corporation 9 West 57th St. New York NY 10019

We present results on the latest advances in thermal infrared face recognition, and its use in combination with visible imagery. Previous research by the authors has shown high performance under very controlled conditions, or questionable performance under a wider range of conditions. this paper shows results on the use of thermal infrared and visible imagery for face recognition in operational scenarios. In particular, we show performance statistics for outdoor face recognition and recognition across multiple sessions. Our results support the conclusion that face recognition performance with thermal infrared imagery is stable over multiple sessions, and that fusion of modalities increases performance. As measured by the number of images and number of subjects, this is the largest ever reported study on thermal face recognition.

关键词： Face recognition

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Face recognition in the dark

Face recognition in the dark

引用

2004 ieee computer Society conference on computer vision and pattern recognition Workshops, cvprW 2004

作者： Selinger, Andrea Socolinsky, Diego A. Equinox Corporation 9 West 57th St New YorkNY10019 United States Equinox Corporation 207 East Redwood St BaltimoreMD21202 United States

Previous research has established thermal infrared imagery of faces as a valid biometric and has shown high recognition performance in a wide range of scenarios. However, all these results have been obtained using eye locations that were either manually marked, or automatically detected in a coregistered visible image, making the realistic use of thermal infrared imagery alone impossible. In this paper we present the results of an eye detector on thermal infrared imagery and we analyze its impact on recognition performance. Our experiments show that although eyes cannot be detected as reliably in thermal images as in visible ones, some face recognition algorithms can still achieve adequate performance. © 2004 ieee.

关键词： Face recognition

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Generating mosaics with minimum distortions

Generating mosaics with minimum distortions

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2004 ieee computer Society conference on computer vision and pattern recognition Workshops, cvprW 2004

作者： Feldman, Doron Zomet, Assaf School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem91904 Israel Computer Science Department Columbia University 500 West 120th Street New YorkNY10027 United States

Manifold mosaicing is a fast and robust way to summarize video sequences captured by a moving camera. It is also useful for rendering compelling 3D visualizations from a video without estimating the 3D structure of the scene. However, since the result mosaics are not perspective images, their geometry is inherently distorted. these mosaics are commonly referred to as multi-perspective images, or multi-perspective mosaics. In this paper we address the following question: Given a video captured by a moving camera, what is the best multi-perspective mosaic that can be generated from it? What is the mosaic with the best combination of large field-of-view and minimal geometric distortions? We define the necessary conditions for a good mosaic and a quantitive criterion for the geometric distortions, and derive analytically the optimal mosaic under this criterion. Results on video sequences confirm that indeed the optimal mosaic has significantly better quality than those generated by other techniques. © 2004 ieee.

关键词： Geometry

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1^st Order class separability using EEG-based features for classification of wrist movements with direction selectivity

1<SUP>st</SUP> Order class separability using EEG-based feat...

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26th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society

作者： Meckes, MP Sepulveda, F Conway, BA Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England

ISBN: (纸本)0780384393

28 channel EEG data were recorded while a subject performed wrist movements in four directions. Four feature types were extracted for each channel following optimized filtering of the signals. the potential performance of each feature and channel for use in the classification of the EEG signals was analyzed by estimating the relative class overlap using a first order histogram approach. the best feature/channel configurations contained channels both that were close and far from motor areas. While the scope and depth of the study was very limited, the results do suggest more attention should be paid to non-motor areas when investigating movement related EEG.

关键词： brain-computer interface movement related potentials pattern recognition EEG

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Optimised attribute selection for emotion classification using physiological signals

Optimised attribute selection for emotion classification usi...

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26th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society

作者： Leon, E Clarke, G Sepulveda, F Callaghan, V Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England

ISBN: (纸本)0780384393

Researchers in medicine and psychology have studied emotions and the way they influence human thinking and behaviour for decades. Recently computer scientists have realised the importance of emotions in human interactions with the environment and a considerable amount of research has been directed towards the identification and utilisation of affective information. Particular interest exists in the detection of emotional states with the intention of improving both human-machine interaction and artificial human-like inference models. Emotion detection has also been employed to explore applications that relate emotional states, habits and ambient conditions inside inhabited environments. Valuable information can be obtained by analysing the way affective states that influence behaviour are altered by environmental changes. In this paper an analysis of the properties of four physiological signals employed in emotion recognition is presented. Class separation analysis was used for determining the best physiological parameters (among those from a list chosen a priori) to use for recognizing emotional states. Results showed that the masseter electromyogram was the best attribute when distinguishing between neutral and non-neutral emotional states. Using Autoassociative Neural Networks for improving cluster separation, the gradient of the skin conductance provided the best results when discriminating between positive and negative emotions.

关键词： cluster analysis emotions pattern recognition physiological signals

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Spectroscopic studies of mitochondrial NADH fluorescence signals in brain slices

Spectroscopic studies of mitochondrial NADH fluorescence sig...

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26th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society

作者： Qiu, LQ Zhao, WZ Sick, T Univ Miami Dept Biomed Engn Coral Gables FL 33124 USA

ISBN: (纸本)0780384393

Dysfunction of mitochondria links a variety of central nervous system (CNS) disorders and other neurodegenerative diseases. the primary respiratory chain substrate reduced form nicotinamide adenine dinucleotide (NADH) is an important regulator of respiratory chain function in mitochondria and, because of its fluorescent properties, has been used to assess mitochondrial pathophysiology in cells and tissues. However, assessment of changes in tissue NADH has been limited to qualitative analysis primarily because hemoglobin (Hb) interferes with NADH fluorescence measurements by absorbing both excitation and emission light. this study presents a computer-assisted approach to estimate brain tissue NADH and Hb concentrations quantitatively at the same time. the method is based on a two-dimensionally interpolated database model that is calibrated by fluorescence emission spectra with known-value standard chemical solutions. Quantitative concentrations for NADH and Hb can be determined by the corresponding known-value spectral data that have the minimum error to the sample spectrum obtained from an experiment. Repeatability and reliability tests are also presented in this report. Results demonstrate that this method can feasibly quantify the NADH content regardless of the Hb background in living hippocampal cells during hypoxia, suggesting that it has potential to be applied to in vivo experiments in the future.

关键词： NADH hemoglobin fluorescence pattern recognition interpolation

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26th German Association for pattern recognition Symposium, DAGM 2004

引用

26th DAGM Symposium on pattern recognition, 2004

ISBN: (纸本)3540229450

the proceedings contain 70 papers. the special focus in this conference is on Learning, Bayesian Approaches, vision and Faces. the topics include: Predictive discretization during model selection;adaptive feature selection in image segmentation;semi-supervised kernel regression using whitened function classes;kernel density estimation and intrinsic alignment for knowledge-driven segmentation;efficient approximations for support vector machines in object detection;efficient computation of optical flow using the census transform;poi detection using channel clustering and the 2d energy tensor;hierarchical image segmentation based on semidefinite programming;scale-invariant object categorization using a scale-adaptive mean-shift search;phase based image reconstruction in the monogenic scale space;minover revisited for incremental support-vector-classification;SVM-based feature selection by direct objective minimisation;learning from labeled and unlabeled data using random walks;multivariate regression via stiefel manifold constraints;shape from shading under coplanar light sources;silhouette based human motion estimation;cooperative optimization for energy minimization in computer vision;a stratified self-calibration method for a stereo rig in planar motion with varying intrinsic parameters;recognition of deictic gestures with context;accurate and efficient approximation of the continuous Gaussian scale-space;a statistical measure for evaluating regions-of-interest based attention algorithms;an algorithm for fast pattern recognition with random spikes;level set based image segmentation with multiple regions;robust pose estimation for arbitrary objects in complex scenes;a probabilistic framework for robust and accurate matching of point clouds and snake-aided automatic organ delineation.

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Robust subspace clustering by combined use of kNND metric and SVD algorithm

Robust subspace clustering by combined use of kNND metric an...

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conference on computer vision and pattern recognition (cvpr)

作者： Qifa Ke T. Kanade Computer Science Department Carnegie Mellon University USA

Subspace clustering has many applications in computer vision, such as image/video segmentation and pattern classification. the major issue in subspace clustering is to obtain the most appropriate subspace from the given noisy data. Typical methods (e.g., SVD, PCA, and eigen-decomposition) use least squares techniques, and are sensitive to outliers. In this paper, we present the k-th nearest neighbor distance (kNND) metric, which, without actually clustering the data, can exploit the intrinsic data cluster structure to detect and remove influential outliers as well as small data clusters. the remaining data provide a good initial inlier data set that resides in a linear subspace whose rank (dimension) is upper-bounded. Such linear subspace constraint can then be exploited by simple algorithms, such as iterative SVD algorithm, to (1) detect the remaining outliers that violate the correlation structure enforced by the low rank subspace, and (2) reliably compute the subspace. As an example, we apply our method to extracting layers from image sequences containing dynamically moving objects.

关键词： Robustness Clustering algorithms Iterative algorithms Application software computer vision Image segmentation pattern classification Principal component analysis Least squares methods Nearest neighbor searches

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