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Multiperspective thermal IR and video arrays for 3D body tracking and driver activity analysis

Multiperspective thermal IR and video arrays for 3D body tra...

引用

2005 IEEE computer Society Conference on computer vision and Pattern Recognition, CVPR 2005 - Workshops

作者： Cheng, Shinko Y. Park, Sangho Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego San DiegoCA United States

ISBN: (纸本)0769526608

This paper presents a multi-perspective (i.e., four camera views) multi-modal (i.e., thermal infrared and color) video based system for robust and real-time 3D tracking of important body *** multi-perspective characteristics of the system provides 3Dtrajectory of the body parts, while the multi-modal characteristics of the system provides robustness and reliability of feature detection and tracking. The application context for this research is that of intelligent vehicles and driver assistance systems. Experimental results demonstrate effectiveness of the proposed system. © 2005 IEEE computer Society. All rights reserved.

关键词： Automobile drivers

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Color reduction by using a new self-growing and self-organized neural network

Color reduction by using a new self-growing and self-organiz...

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2nd International Conference on Video, vision and Graphics, VVG 2005

作者： Atsalakis, A. Papamarkos, N. Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace 67100 Xanthi Greece

ISBN: (纸本)9783905673579

A new method for the reduction of the number of colors in a digital image is proposed. The new method is based on the developed of a new neural network classifier that combines the advantages of the Growing Neural Gas (GNG) and the Kohonen Self-Organized Feature Map (SOFM) neural networks. We call the new neural network: Self-Growing and Self-Organized Neural Gas (SGONG). Its main advantage is that it defines the number of the created neurons and their topology in an automatic way. As a consecutive, isolated color classes, which may correspond to significant image details, can be obtained. The SGONG is fed by the color components and additional spatial features. To speed up the entire algorithm and to reduce memory requirements, a fractal scanning sub-sampling technique is used. The method is applicable to any type of color images and it can accommodate any type of color space. © The Eurographics Association 2005.

关键词： Color

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On using an associative memory for improving digital color images: Color characterization, enhancement, and color balancing

On using an associative memory for improving digital color i...

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International Joint Conference on Neural Networks, IJCNN 2005

作者： Seow, Ming-Jung Asari, Vijayan K. Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA 23529

Actual observed scenes usually produce a wide dynamic range. Currently available visual systems use low dynamic range light detector that provide only 8 bits of brightness information at each pixel. This greatly limits what visual systems can do for surveillance applications such as face detection and face recognition. A color image enhancement procedure based on the concept of color characterization, enhancement, and color balancing is proposed in this paper. The enhancement technique directly operates on pixels using a hyperbolic tangent function to increase the dynamic range of the pixel. The global and local statistics of the image is used to control the curvature of the hyperbolic tangent function. The color characterization and color balancing processes are based on a new nonlinear line attractor network to create a color manifold to restore the relationship of red, green, and blue components of the pixels. The proposed enhancement approach greatly improves the dynamic range compression and color rendition of an image. © 2005 IEEE.

关键词： Color image processing

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A robust image enhancement technique for improving image visual quality in shadowed scenes

A robust image enhancement technique for improving image vis...

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4th International Conference on image and Video Retrieval, CIVR 2005

作者： Tao, Li Asari, Vijayan K. Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA 23529 United States

An effective and robust image enhancement algorithm is presented for improving the visual quality of digital images captured under extremely low or non-uniform lighting conditions. The proposed algorithm is composed of two separated processes viz. adaptive luminance enhancement and adaptive contrast enhancement to provide a more flexible and better control over the image enhancement. Adaptive luminance enhancement is an intensity transformation based on a specifically designed nonlinear transfer function which largely increases the luminance of darker pixels and compresses the dynamic range as well. Adaptive contrast enhancement adjusts the intensity of each pixel based on its relative magnitude with respect to the neighboring pixels. Both processes can be self-tuned by the image statistical information. A proportional color restoration process is applied to convert the enhanced intensity image back to a color image. Real time processing and embedded application in mobile device have been successfully realized. © Springer-Verlag Berlin Heidelberg 2005.

关键词： image enhancement

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A visibility improvement system for low vision drivers by nonlinear enhancement of fused visible and infrared video

A visibility improvement system for low vision drivers by no...

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2005 IEEE computer Society Conference on computer vision and Pattern Recognition, CVPR 2005 - Workshops

作者： Ngo, Hau Tao, Li Zhang, Ming Livingston, Adam Asari, Vijayan Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States

ISBN: (纸本)0769526608

Development of a visibility improvement system for helping drivers with poor vision during night and bad weather conditions is proposed in this paper. Video streams captured by a CCD camera and an infrared camera are registered and fused to obtain image details in extremely low lighting conditions. A novel image enhancement technique employing a nonlinear expansion function is developed for enhancing the fused images. The slope characteristics of the expansion function at an individual pixel location is based on the statistical properties of its neighborhood pixels. The expansion function is also capable of reducing the intensity of the overly bright image regions due to the presence of head lights from vehicles in opposite direction. The enhanced video stream is then subjected to a color restoration process to introduce natural color using the color information gathered from the CCD camera image. The entire image processing and analysis system is being installed in an FPGA environment for performing the processing in real-time. The processed video stream is displayed on a Head-Up Display located directly in front of the driver to assist him/her to drive safely in poor visibility environments. Preliminary results obtained in various experiments conducted with the proposed system are encouraging. © 2005 IEEE computer Society. All rights reserved.

关键词： CCD cameras

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Mixed PCA/ICA spectral/spatial compression for hyperspectral imagery

Mixed PCA/ICA spectral/spatial compression for hyperspectral...

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Chemical and Biological Standoff Detection III

作者： Wang, Jing Chang, Chein-I. Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250

Principal components analysis (PCA) has been widely used in many applications, particularly, data compression. Independent component analysis (ICA) has been also developed for blind source separation along with many other applications such as channel equalization, speech processing. Recently, it has been shown that the ICA can be also used for hyperspectral data compression. This paper investigates these two transforms in hyperspectral data compression and further evaluates their strengths and weaknesses in applications of target detection, mixed pixel classification and abundance quantification. In order to take advantage of the strengths of both transform, a new transform, called mixed PCA/ICA transform is developed in this paper. The idea of the proposed mixed PCA/ICA transform is derived from the fact that it can integrate different levels of information captured by the PCA and ICA. In doing so, it combines m principal components (PCs) resulting from the PCA and n independent components (ICs) generated by the ICA to form a new set of (m+n) mixed components used for hyperspectral data compression. The resulting transform is referred to as mixed (m,n)-PCA/ICA transform. In order to determine the total number of components, p needed to be generated for the mixed (m,m)-PCA/ICA transform, a recently developed virtual dimensionality (VD) is introduced to estimate the p where p = m + n. If m = p and n = 0, then mixed (m,n)-PCA/ICA transform is reduced to PCA transform. On the other hand, if m = 0 and n = p, then mixed (m,n)-PCA/ICA transform is reduced to ICA. Since various combinations of m and n have different impacts on the performance of the mixed PCA/ICA spectral/spatial compression in applications, experiments based on subpixel detection and mixed pixel quantification are conducted for performance evaluation.

关键词： image compression

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A multi-view approach on modular PCA for illumination and pose invariant face recognition

A multi-view approach on modular PCA for illumination and po...

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33rd Applied imagery Pattern Recognition Workshop

作者： Sankaran, Praveen Asari, Vijayan Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA 23529-0246 United States

ISBN: (纸本)0769522505

A modified approach on Modular PCA for face recognition is presented in this paper. The proposed changes aim to improve the recognition rates for modular PCA for face images with large variation in light and facial expression. The eyes form one of the most invariant regions on the face. A sub-image from this region is considered. Weight vectors from this region are appended to the existing weight vector for Modular PCA. The accuracies for the modified method, the original method and PCA method are evaluated under conditions of varying pose, illumination and expressions using standard face databases. © 2005 IEEE.

关键词： Face recognition

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Skew estimation and correction for form documents using wavelet decomposition

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2nd International Conference on image analysis and Recognition, ICIAR 2005

作者： Xi, Dihua Kamel, Mohamed Lee, Seong-Whan Pattern Analysis and Machine Intelligence Research Group Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Center for Artificial Vision Research Korea University Seoul Korea Republic of

ISBN: (纸本)3540290699

Form document image processing has become an increasingly essential technology in office automation tasks. One of the problems is that the document image may appear skewed for many reasons. Therefore, the skew estimation plays an important role in any automatic document analysis system. In the past few years, many algorithms have been developed to detect the skew angle of text document images. However, these algorithms suffer from two major deficiencies. Firstly, most of them suppose that the original image is monochrome and therefore they are not suitable to apply to documents with a complicated background. Secondly, most of the current methods were developed for general document images that are not as complicated as form documents. In this paper, we present a new approach to skew detection for grey-level form document images. In our system, image decomposition by 2D wavelet transformations is used to estimate the skew angle. © Springer-Verlag Berlin Heidelberg 2005.

关键词： image processing

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Extracting the optic disk endpoints in optical coherence tomography data

Extracting the optic disk endpoints in optical coherence tom...

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7th IEEE Workshop on Applications of computer vision, WACV 2005

作者： Herzog, Artemas Boyer, Kim L. Roberts, Cynthia Ohio State University Department of Electrical and Computer Engineering Signal Analysis and Machine Perception Laboratory United States Ohio State University Department of Ophthalmology Department of Biomedical Engineering United States

ISBN: (纸本)0769522718

Optical Coherence Tomography (OCT) uses backre-flected light to provide micron-resolution, cross-sectional scans of biological tissues useful in diagnosing, monitoring, and studying glaucoma. The ratio of the area of the central portion of the optic nerve (the cup) to that of the complete nerve (the disk) is an indicator of ocular health. Measuring this cup-to-disk ratio accurately requires precise detection of the cup and disk limits in scans traversing the optic nervehead. A hybrid edge-supported boundary detector first finds the contour separating the retina and optic nervehead (on one side) from the vitreous humor on the other. We then partition this boundary using a model-based technique to identify the optic cup. The lower retinal-choroid boundaries are extracted using Markov models first derived by Koozekanani et al.. The endpoints of these lower boundaries represent the limits of the optic disk. Points along the Markov path are then represented in a feature space comprising two low order unitary transform coefficients and a measure of differential edge strength. By minimizing the trace of the within-class scatter matrices for optic nerve and retina, optimal estimates of the nervehead extent are found. We present thorough experimental results to establish the stability and robustness of the technique, as well as its performance limitations.

关键词： Optical tomography

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View matching with blob features

View matching with blob features

引用

Canadian Conference on computer and Robot vision, CRV

作者： P.E. Forssen A. Moe Computer Vision Laboratory Department of Electrical Engineering Linkoping University Sweden

This paper introduces a new region based feature for object recognition and image matching. In contrast to many other region based features, this one makes use of colour in the feature extraction stage. We perform experiments on the repeatability rate of the features across scale and inclination angle changes, and show that avoiding to merge regions connected by only a few pixels improves the repeatability. Finally we introduce two voting schemes that allow us to find correspondences automatically, and compare them with respect to the number of valid correspondences they give, and their inlier ratios.

关键词： Robustness Object recognition computer vision Feature extraction Mathematics Symmetric matrices Transmission line matrix methods Detection algorithms Clustering algorithms Pixel

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