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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3931-3940 订阅

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Illumination normalization for face recognition and uneven background correction using total variation based image models

Illumination normalization for face recognition and uneven b...

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conference on computer vision and pattern recognition (cvpr)

作者： T. Chen W. Yin X.S. Zhou D. Comaniciu T.S. Huang University of Illinois Urbana-Champaign USA Columbia University USA Siemens Corporate Research Inc. USA

We present a new algorithm for illumination normalization and uneven background correction in images, utilizing the recently proposed TV+L/sup 1/ model: minimizing the total variation of the output cartoon while subject to an L/sup 1/-norm fidelity term. We give intuitive proofs of its main advantages, including the well-known edge preserving capability, minimal signal distortion, and scale-dependent but intensity-independent foreground extraction. We then propose a novel TV-based quotient image model (TVQI) for illumination normalization, an important preprocessing for face recognition under different lighting conditions. Using this model, we achieve 100% face recognition rate on Yale face database B if the reference images are under good lighting condition and 99.45% if not. These results, compared to the average 65% recognition rate of the quotient image model and the average 95% recognition rate of the more recent self quotient image model, show a clear improvement. In addition, this model requires no training data, no assumption on the light source, and no alignment between different images for illumination normalization. We also present the results of the related applications - uneven background correction for cDNA mic roar ray films and digital microscope images. We believe the proposed works can serve important roles in the related fields.

关键词： Lighting Face recognition Light sources Distortion Image recognition Microscopy Image databases Training data Surface texture computer vision

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Shape recognition based on a video and multi-sensor system

Shape recognition based on a video and multi-sensor system

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ieee conference on Advanced Video and Signal Based Surveillance, AVSS 2005

作者： Bui Ngoc, Huy-Binh Bremond, François Thonnat, Monique Faure, Jean-Claude RATP SIT/SVO/BRS/CTSC Noisy Le Grand France ORION Research Team INRIA Sophia-Antipolis France

ISBN: (纸本)0780393856

We present in this paper a real-time system for shape recognition. The proposed system is a video and multi-sensor platform that is able to classify the mobile objects evolving in the scene into several expected categories. The key of the recognition method is to compute mobile object properties thanks to the camera and sensors and then to use Bayesian classifiers. A learning phase based on ground truth data is used to train the Bayesian classifiers. Our recognition method has been integrated into an existing access control device used in public transportation (subway) at RATP (Régie Autonome des Transports Parisiens) to improve safety and comfort, to prevent fraud and to count people for statistical matters. The expected categories in this case are mainly "adult", "child", "suitcase" and "two adults close to each other". © 2005 ieee.

关键词： pattern recognition

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Semi-supervised cross feature learning for semantic concept detection in videos

Semi-supervised cross feature learning for semantic concept ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Rong Yan M. Naphade School of Computer Science Carnegie Mellon University Pittsburgh PA USA IBM Thomas J. Watson Research Center Hawthorne NY USA

For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. But a major obstacle to this is the insufficiency of labeled training samples. Multi-view semi-supervised learning algorithms such as co-training may help by incorporating a large amount of unlabeled data. However, one of their assumptions requiring that each view be sufficient for learning is usually violated in semantic concept detection. In this paper, we propose a novel multi-view semi-supervised learning algorithm called semi-supervised cross feature learning (SCFL). The proposed algorithm has two advantages over co-training. First, SCFL can theoretically guarantee its performance not being significantly degraded even when the assumption of view sufficiency fails. Also, SCFL can also handle additional views of unlabeled data even when these views are absent from the training data. As demonstrated in the TRECVID '03 semantic concept extraction task, the proposed SCFL algorithm not only significantly outperforms the conventional co-training algorithms, but also comes close to achieving the performance when the unlabeled set were to be manually annotated and used for training along with the labeled data set.

关键词： Videos Semisupervised learning Data mining Speech Gunshot detection systems computer science Drives Large-scale systems Degradation Training data

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A principal component neural network-based face recognition system and its ASIC implementation

A principal component neural network-based face recognition ...

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18th International conference on VLSI Design: Power Aware Design of VLSI Systems

作者： Prasanna, Chakka Siva Sai Sudha, N. Kamakoti, V. Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)0769522645

Principal Component Analysis (PCA) finds wide usage in computer-aided vision applications and one such application is face recognition. The neural network that performs PCA is called a Principal Component Neural Network (PCNN). This paper presents a new PCNN-based face recognition system. The proposed recognition system can tolerate local variations in the face such as expression changes and directional lighting. An optimal digital hardware design is proposed for PCNN. An ASIC implementation of the proposed design yields a throughput of processing about 11,000 inputs per second during the training phase and about 19,000 inputs per second during the retrieval phase. The customized hardware-based recognition is about 105 times faster than a software-based recognition in a PC. Such results are valuable for high-speed applications. © 2005 ieee.

关键词： Application specific integrated circuits

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A Prescreener for 3D Face recognition Using Radial Symmetry and the Hausdorff Fraction

A Prescreener for 3D Face Recognition Using Radial Symmetry ...

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

作者： M.L. Koudelka M.W. Koch T.D. Russ Sensor Exploitation Apps Sandia National Laboratories Albuquerque NM USA Security Technology Sandia National Laboratories Albuquerque NM USA

Face recognition systems require the ability to efficiently scan an existing database of faces to locate a match for a newly acquired face. The large number of faces in real world databases makes computationally intensive algorithms impractical for scanning entire databases. We propose the use of more efficient algorithms to "prescreen" face databases, determining a limited set of likely matches that can be processed further to identify a match. We use both radial symmetry and shape to extract five features of interest on 3D range images of faces. These facial features determine a very small subset of discriminating points which serve as input to a prescreening algorithm based on a Hausdorff fraction. We show how to compute the Haudorff fraction in linear O(n) time using a range image representation. Our feature extraction and prescreening algorithms are verified using the FRGC v1.0 3D face scan data. Results show 97% of the extracted facial features are within 10 mm or less of manually marked ground truth, and the prescreener has a rank 6 recognition rate of 100%.

关键词： Face recognition Image databases Facial features Probes Spatial databases Sensor systems Laboratories Shape Feature extraction Data mining

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OBJ CUT

OBJ CUT

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conference on computer vision and pattern recognition (cvpr)

作者： M.P. Kumar P.H.S. Ton A. Zisserman Dept. of Computing Oxford Brookes University Oxford Brookes University Oxford Oxfordshire GB Dept. of Comput. Oxford Brookes Univ. UK Dept. of Engineering Science University of Oxford UK

In this paper, we present a principled Bayesian method for detecting and segmenting instances of a particular object category within an image, providing a coherent methodology for combining top down and bottom up cues. The work draws together two powerful formulations: pictorial structures (PS) and Markov random fields (MRFs) both of which have efficient algorithms for their solution. The resulting combination, which we call the object category specific MRF, suggests a solution to the problem that has long dogged MRFs namely that they provide a poor prior for specific shapes. In contrast, our model provides a prior that is global across the image plane using the PS. We develop an efficient method, OBJ CUT, to obtain segmentations using this model. Novel aspects of this method include an efficient algorithm for sampling the PS model, and the observation that the expected log likelihood of the model can be increased by a single graph cut. Results are presented on two object categories, cows and horses. We compare our methods to the state of the art in object category specific image segmentation and demonstrate significant improvements.

关键词： Image segmentation Shape Object recognition Optimization methods Bayesian methods Markov random fields Horses Object detection Image sampling Cows

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Integrating LDV Audio and IR Video for Remote Multimodal Surveillance

Integrating LDV Audio and IR Video for Remote Multimodal Sur...

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

作者： Zhigang Zhu Weihong Li G. Wolberg Department of Computer Science City College and Graduate Center City University of New York NY USA

This paper describes a multimodal surveillance system for human signature detection. The system consists of three types of sensors: infrared (IR) cameras, pan/tilt/zoom (PTZ) color cameras and laser Doppler vibrometers (LDVs). The LDV is explored as a new non-contact remote voice detector. We have found that voice energy vibrates most objects and the vibrations can be detected by an LDV. Since signals captured by the LDV are very noisy, we have designed algorithms with Gaussian bandpass filtering and adaptive volume scaling to enhance the LDV voice signals. The enhanced voice signals are intelligible from targets without retro-reflective finishes at short or medium distances (<100m). By using retroreflective tapes, the distance could be as far as 300 meters. However, the manual operation to search and focus the laser beam on a target with both vibration and reflection is very difficult at medium and large distances. Therefore, infrared (IR) imaging for target selection and localization is also discussed. Future work remains in automatic LDV targeting and intelligent refocusing for long range LDV listening.

关键词： Surveillance Infrared sensors Cameras Humans Sensor systems Vibrometers Detectors Object detection Gaussian noise Signal design

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Bi-layer segmentation of binocular stereo video

Bi-layer segmentation of binocular stereo video

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conference on computer vision and pattern recognition (cvpr)

作者： V. Kolmogorov A. Criminisi A. Blake G. Cross C. Rother Microsoft Research Limited Cambridge UK

This paper describes two algorithms capable of real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from colour/contrast or from stereo alone is known to be error-prone. Here, colour, contrast and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, layered dynamic programming (LDP), solves stereo in an extended 6-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive colour model that is learned on the fly, and stereo disparities are obtained by dynamic programming. The second algorithm, layered graph cut (LGC), does not directly solve stereo. Instead the stereo match likelihood is marginalised over foreground and background hypotheses, and fused with a contrast-sensitive colour model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar p performance, substantially better than stereo or colour/contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.

关键词： Image segmentation Dynamic programming Computational efficiency Streaming media Video sequences Heuristic algorithms Application software Teleconferencing computer graphics Degradation

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Rank-R approximation of tensors using image-as-matrix representation

Rank-R approximation of tensors using image-as-matrix repres...

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conference on computer vision and pattern recognition (cvpr)

作者： H. Wang Narendra Ahuja Beckman Institute University of Illinois Urbana-Champaign USA

We present a novel multilinear algebra based approach for reduced dimensionality representation of image ensembles. We treat an image as a matrix, instead of a vector as in traditional dimensionality reduction techniques like PCA, and higher-dimensional data as a tensor. This helps exploit spatio-temporal redundancies with less information loss than image-as-vector methods. The challenges lie in the computational and memory requirements for large ensembles. Currently, there exists a rank-R approximation algorithm which, although applicable to any number of dimensions, is efficient for only low-rank approximations. For larger dimensionality reductions, the memory and time costs of this algorithm become prohibitive. We propose a novel algorithm, for rank-R approximations of third-order tensors, which is efficient for arbitrary R but for the important special case of 2D image ensembles, e.g. video. Both of these algorithms reduce redundancies present in all dimensions. Rank-R tensor approximation yields the most compact data representation among all known image-as-matrix methods. We evaluated the performance of our algorithm vs. other approaches on a number of datasets with the following two main results. First, for a fixed compression ratio, the proposed algorithm yields the best representation of image ensembles visually as well as in the least squares sense. Second, proposed representation gives the best performance for object classification.

关键词： Tensile stress Principal component analysis Covariance matrix Least squares approximation Redundancy Algebra Image coding computer vision Facial animation Face recognition

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Summation invariant and its applications to shape recognition

Summation invariant and its applications to shape recognitio...

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2005 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Lin, Wei-Yang Boston, Nigel Hu, Yu Hen Department of Electrical and Computer Engineering University of Wisconsin Madison WI 53706 United States

ISBN: (纸本)0780388747

A novel summation invariant of curves under transformation group action is proposed. This new invariant is less sensitive to noise than the differential invariant and does not require an analytical expression for the curve as the integral invariant does. We exploit this summation invariant to define a shape descriptor called a semi-local summation invariant and use it as a new feature for shape recognition. Tested on a database of noisy shapes of fishes, it was observed that the summation invariant feature exhibited superior discriminating power than that of wavelet-based invariant features. © 2005 ieee.

关键词： pattern recognition

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