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3D face recognition using two views face modeling and labeling

3D face recognition using two views face modeling and labeli...

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Sun, Yi Yin, Lijun Computer Science Department State University of New York at Binghamton BinghamtonNY13902 United States

ISBN: (纸本)0769526608

The ability to distinguish different people by using 3D facial information is an active research problem being undertaken by the face recognition community. In this paper, we propose to use a generic model to label the 3D facial features. This approach relies on our realistic face modeling technique, by which the individual face model is created using a generic model and two views of a face. In the individualized model, we label the face features by their maximum and minimum curvatures. Among the labeled features, the "good features" are selected by using a Genetic Algorithm based approach. The feature space is then formed by using these new 3D shape descriptors, and each individual face is classified according to its feature space correlation. We applied 72 individual models for the test. The experimental results show that the shape information obtained from the 3D individualized model can be used to classify and identify individual facial surfaces. The rank-four correct recognition rate is about 92%. The 3D individualized model provides consistent and sufficient details to represent individual faces while using a much more simplified representation than the range data models. This work provides the possibility to reduce the complexity of 3D data processing, and is feasible for real applications such as in a non-cooperative imaging circumstance or in the situation when range data using 3D scanners are not possible to acquire. © 2005 IEEE computer Society. All rights reserved.

关键词： Face recognition

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Overtaking vehicle detection using dynamic and quasi-static background modeling

Overtaking vehicle detection using dynamic and quasi-static ...

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Wang, Junxian Bebis, George Miller, Ronald Computer Vision Laboratory University of Nevada RenoNV United States Vehicle Design R&A Department Ford Motor Company DearbornMI United States

ISBN: (纸本)0769526608

Robust and reliable detection of overtaking vehicles is an important component of any on-board driver assistance system. Optical flow, with the abundant motion information present in image sequences, has been studied extensively for vehicle detection. However, using dense optical flow for vehicle detection is sensitive to shocks and vibrations of the mobile camera;image outliers caused by illumination changes;and high computational complexity. To improve vehicle detection performance and reduce computational complexity, we propose an efficient and robust methodology for overtaking vehicle detection based on homogeneous sparse optical flow and eigenspace modeling. Specifically, our method models the background into dynamic and quasi-static regions. Instead of using dense optical flow to model the dynamic parts of the background, we employ homogeneous sparse optical flow, which makes detection more robust to camera shocks and vibrations. Moreover, to make detection robust to illumination changes, we employ a block-based eigenspace approach to represent quasi-static regions in the background. A region-based hysteresis-thresholding approach, augmented by a localized spatial segmentation procedure, attains a good tradeoff between true detections and false positives. The proposed methodology has been evaluated using challenging traffic scenes illustrating good performance. © 2005 IEEE computer Society. All rights reserved.

关键词： Optical flows

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Pixel-aligned warping for multiprojector tiled displays

Pixel-aligned warping for multiprojector tiled displays

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Hereld, Mark Stevens, Rick L. Mathematics and Computer Science Division Argonne National Laboratory Computation Institute University of Chicago Argonne National Laboratory Department of Computer Science University of Chicago

ISBN: (纸本)0769526608

Multiprojector tiled displays offer a scalable path to high-resolution display systems, often in a large format. Such displays have been employed in a range of applications from scientific visualization to collaborative virtual environments. Images projected onto these systems have always looked less sharp than expected owing to the image warping required to achieve geometrical alignment of the partial images to form the seamless whole. Similar degradation of image quality accompanies keystone correction techniques and image warping onto nonplanar surfaces used in single projector applications. In this paper we discuss the underlying cause of the degradation of apparent image resolution, and we introduce a new class of warping techniques, called pixel-aligned warping, that tend to preserve image sharpness at the expense of strict adherence to underlying geometrical constraints that define the desired warp. We demonstrate how such techniques can be used to significantly improve the apparent sharpness of the final image. © 2005 IEEE computer Society. All rights reserved.

关键词： Image resolution

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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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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Hidalgo-Sotelo, Barbara Oliva, Aude Torralba, Antonio Department of Brain and Cognitive Sciences MIT CambridgeMA02139 United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA02139 United States

ISBN: (纸本)0769526608

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 context-dependent scene processing and its putative role in various stages of visual search. © 2005 IEEE computer Society. All rights reserved.

关键词： Eye movements

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Articulated pose estimation in a learned smooth space of feasible solutions

Articulated pose estimation in a learned smooth space of fea...

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Tian, Tai-Peng Li, Rui Sclaroff, Stan Computer Science Department Boston University BostonMA02215 United States

ISBN: (纸本)0769526608

A learning based framework is proposed for estimating human body pose from a single image. Given a differentiable function that maps from pose space to image feature space, the goal is to invert the process: estimate the pose given only image features. The inversion is an ill-posed problem as the inverse mapping is a one to many process, hence multiple solutions exist. It is desirable to restrict the solution space to a smaller subset of feasible solutions. The space of feasible solutions may not admit a closed form description. The proposed framework seeks to learn an approximation over such a space. Using Gaussian Process Latent Variable Modelling. The scaled conjugate gradient method is used to find the best matching pose in the learned space. The formulation allows easy incorporation of various constraints for more accurate pose estimation. The performance of the proposed approach is evaluated in the task of upper-body pose estimation from silhouettes and compared with the Specialized Mapping Architecture. The proposed approach performs better than the latter approach in terms of estimation accuracy with synthetic data and qualitatively better results with real video of humans performing gestures. © 2005 IEEE computer Society. All rights reserved.

关键词： Inverse problems

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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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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Zhu, Zhigang Li, Weihong Wolberg, George Department of Computer Science City College and Graduate Center The City University of New York New YorkNY10031 United States

ISBN: (纸本)0769526608

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 ( © 2005 IEEE computer Society. All rights reserved.

关键词： Laser beams

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Unsupervised modeling of signs embedded in continuous sentences

Unsupervised modeling of signs embedded in continuous senten...

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Nayak, Sunita Sarkar, Sudeep Loeding, Barbara Department of Computer Science and Engineering University of South Florida TampaFL33620 United States Department of Special Education University of South Florida Lakeland Campus LakelandFL33803 United States

ISBN: (纸本)0769526608

The common practice in sign language recognition is to first construct individual sign models, in terms of discrete state transitions, mostly represented using Hidden Markov Models, from manually isolated sign samples and then to use it to recognize signs in continuous sentences. In this paper we (i) propose a continuous state space model, where the states are based on purely image-based features, without the use of special gloves, and (ii) present an unsupervised approach to both extract and learn models for continuous basic units of signs, which we term as signemes, from continuous sentences. Given a set of sentences with a common sign, we can automatically learn the model for part of the sign, or signeme, that is least affected by coarticulation effects. While there are coarticulation effects in speech recognition, these effects are even stronger in sign language. The model itself is in term of traces in a space of Relational Distributions. Each point in this space represents a Relational Distribution, capturing the spatial relationships between low-level features, such as edge points. We perform speed normalization and then incrementally extract the common sign between sentences, or signemes, with a dynamic programming framework at the core to compute warped distance between two subsentences. We test our idea using the publicly available Boston SignStream Dataset by building signeme models of 18 signs. We test the quality of the models by considering how well we can localize the sign in a new sentence. We also present preliminary results for the ability to generalize across signers. © 2005 IEEE computer Society. All rights reserved.

关键词： Dynamic programming

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3D and moving target extraction from dynamic pushbroom stereo mosaics

3D and moving target extraction from dynamic pushbroom stere...

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Zhu, Zhigang Tang, Hao Shen, Bing Wolberg, George Department of Computer Science The City College of New York New YorkNY10031 United States

ISBN: (纸本)0769526608

In this paper, we propose a dynamic pushbroom stereo mosaic approach for representing and extracting 3D structures and independent moving targets from urban 3D scenes. Our goal is to acquire panoramic mosaic maps with motion tracking information for 3D (moving) targets using a light aerial vehicle equipped with a video camera flying over an unknown area for urban surveillance. In dynamic pushbroom stereo mosaics, independent moving targets can be easily identified in the matching process of stereo mosaics by detecting the "out-of-place" regions that violate epipolar constraints and/or give 3D anomalies. We propose a segmentation-based stereo matching approach with natural matching primitives to estimate the 3D structure of the scene, particularly the ground structures (e.g., roads) on which humans or vehicles move, and then to identify moving targets and to measure their 3D structures and movements. © 2005 IEEE computer Society. All rights reserved.

关键词： Video cameras

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A binary tree for probability learning in eye detection

A binary tree for probability learning in eye detection

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Wu, Junwen Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San DiegoCA92037 United States

ISBN: (纸本)0769526608

In this paper we proposed to solve the eye detection and localization problem under a general statistical model based object detection framework. A binary tree representation is used to discover the objects' underlying statistical structure. Tree structures enable us to describe the object local statistical structure in a coarse-to-fine fashion. Each subtree explains the statistics for certain local *** tree is built in a top-down fashion. Subsets with negligible conditional independency are found by k-means clustering using mutual information. The conditionally independent features are separated into different subtrees, while more dependent features are tended to appear close in the tree. The distribution of the object can be learned accordingly. Gaussian mixture in the independent component analysis (ICA) subspace is used to model the distribution of each high dependent feature subset, where each independent component explains the local substructure. The use of tree structure enables us to learn the distribution recursively by applying Bayesian criterion. Substantial experiments were done to evaluate the performance over the eyes detection accuracy as well as the localization ability. Experimental results show a better detection accuracy than the Viisage system with a reasonable localization ability, which validate the algorithm. © 2005 IEEE computer Society. All rights reserved.

关键词： Object detection

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A combinational approach to the fusion, de-noising and enhancement of dual-energy x-ray luggage images

A combinational approach to the fusion, de-noising and enhan...

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Chen, Zhiyu Zheng, Yue Abidi, Besma R. Page, David L. Abidi, Mongi A. Imaging Robotics and Intelligent Systems Laboratory Electrical and Computer Engineering Department University of Tennessee KnoxvilleTN37996 United States

ISBN: (纸本)0769526608

X-ray luggage inspection systems play an important role in ensuring air travelers' security. However, the false alarm rate of commercial systems can be as high as 20% due to less than perfect image processing algorithms. In an effort to reduce the false alarm rate, this paper proposes a combinational scheme to fuse, de-noise and enhance dual-energy X-ray images for better object classification and threat detection. The fusion step is based on the wavelet transform. Fused images generally reveal more detail information;however, background noise often gets amplified during the fusion process. This paper applies a background-subtraction-based noise reduction technique which is very efficient in removing background noise from fused X-ray images. The de-noised image is then processed using a new enhancement technique to reconstruct the final image. The final image not only contains complementary information from both source images, but is also background-noise-free and contrast-enhanced, therefore easier to segment automatically or be interpreted by screeners, thus reducing the false alarm rate in X-ray luggage inspection. © 2005 IEEE computer Society. All rights reserved.

关键词： Wavelet transforms

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