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检索条件"机构=Image processing and Intelligent Systems Laboratory"

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Ethnicity classification based on gait using multi-view fusion

Ethnicity classification based on gait using multi-view fusi...

引用

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010

作者： Zhang, De Wang, Yunhong Bhanu, Bir Intelligent Recognition and Image Processing Laboratory Beihang University Beijing 100191 China Center for Research in Intelligent Systems University of California Riverside CA 92521 United States

ISBN: (纸本)9781424470297

The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view gait. Gait Energy image (GEI) is used to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x;y;view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs thebest. © 2010 IEEE.

关键词： Principal component analysis

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Wavelet-based enhancement of color image using combined local variance and entropy analysis

Wavelet-based enhancement of color image using combined loca...

引用

2010 International Conference on Consumer Electronics, ICCE 2010

作者： Jun, Sinyoung Lee, Eunsung Kim, Sangjin Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781424443161

This paper presents a novel wavelet-based image enhancement approach to reduce noise in a single-sensor color camera. Most traditional noise reduction methods tend to over-suppress high-frequency details. For overcoming this problem we first decompose the input image into flat and edge regions, and adaptively remove noise by multiplying alpha map and wavelet transform coefficients of LH, HL, and HH bands. After removing noise in the flat region, we further remove noise in edge regions by adaptive shrink of wavelet coefficients based on the entropy. Experimental result show the proposed algorithm is proved to be able to reduce noise without losing sharp details and is suitable for commercial low-cost such as digital cameras, CCTV, and surveillance systems. ©2010 IEEE.

关键词： image enhancement

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image restoration using alpha map-based fusion for multiple color aperture computational camera

Image restoration using alpha map-based fusion for multiple ...

引用

2010 International Conference on Consumer Electronics, ICCE 2010

作者： Lee, Eunsung Maik, Vivek Jun, Sinyoung Kim, Sangjin Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781424443161

We present a novel image restoration approach by fusing multiple images acquired by a multiple color aperture computational camera for overcoming the limit of conventional image restoration algorithms, such as high computational load and post-processing artifacts. The proposed restoration framework utilizes color matching, alignment, and fusion of multiple images acquired by using multiple color apertures. The alpha map-based image fusion step successfully restores high-frequency components of meaningful edge regions. The proposed scheme can achieve automatic focusing and varying depth of filed at low cost for low-end consumer cameras. ©2010 IEEE.

关键词： Color

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Age classification based on gait using HMM

Age classification based on gait using HMM

引用

2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Zhang, De Wang, Yunhong Bhanu, Bir Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing 100191 China Center for Research in Intelligent Systems University of California Riverside CA 92521 United States

ISBN: (纸本)9780769541099

In this paper we propose a new framework for age classification based on human gait using Hidden Markov Model (HMM). A gait database including young people and elderly people is built. To extract appropriate gait features, we consider a contour related method in terms of shape variations during human walking. Then the image feature is transformed to a lower-dimensional space by using the Frame to Exemplar (FED) distance. A HMM is trained on the FED vector sequences. Thus, the framework provides flexibility in the selection of gait feature representation. In addition, the framework is robust for classification due to the statistical nature of HMM. The experimental results show that video-based automatic age classification from human gait is feasible and reliable. © 2010 IEEE.

关键词： Hidden Markov models

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A fully digital auto-focusing system using a priori point spread function dataset

A fully digital auto-focusing system using a priori point sp...

引用

IEEE International Conference on Consumer Electronics (ICCE)

作者： Younguk Park Jaehwan Jeon Jinhee Lee Joonki Paik Image Processing and Intelligent Systems Laboratory Chung-Ang University Seoul South Korea

ISBN: (纸本)9781424443147

This paper presents a fully digital auto-focusing (FDAF) system using a priori obtained point spread function (PSF) dataset. The proposed FDAF algorithm aims at low-cost, affordable consumer's imaging electronics free from either optical or mechanical focusing parts. The proposed algorithm consists of three steps: i) a priori estimation of a set of PSFs, ii) automatic selection of a proper PSF from the blurred input image, and iii) restoration the out-of-focused image by selecting the optimal PSF from the a priori obtained dataset. The proposed algorithm can realize low-cost, high-quality imaging systems, such as digital still cameras, mobile camera phones, and surveillance cameras.

关键词： Digital cameras Focusing image restoration Equations Optical imaging Yield estimation Vectors image databases image processing intelligent systems

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Weighted image defogging method using statistical RGB channel feature extraction

Weighted image defogging method using statistical RGB channe...

引用

International SoC Design Conference, ISOCC

作者： Inhye Yoon Jaehwan Jeon Jinhee Lee Joonki Paik Department of Image Processing and Intelligent Systems Laboratory Chung-Ang University Seoul South Korea

In this paper, we present a weighted adaptive image defogging method by extracting features in the RGB color channels. We adaptively detect an atmospheric light through undesired fog in the dark channel prior obtained in the YCbCr color channels and generate a transmission map based on the detected atmospheric light. We adaptively remove the fog by applying the color correction algorithm based on the feature extraction in the RGB color channels. The proposed algorithm can overcome the problem of local color distortion, which is known to be the limitations of existing defogging techniques. Experimental results demonstrate that the proposed algorithm can remove image degradation caused by fog, clouds, smoke, and dust in digital imaging devices.

关键词： image color analysis Feature extraction Pixel image restoration image edge detection Channel estimation Clouds

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Ethnicity classification based on gait using multi-view fusion

Ethnicity classification based on gait using multi-view fusi...

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： De Zhang Yunhong Wang Bir Bhanu Center for Research in Intelligent Systems University of California Riverside CA USA Intelligent Recognition and Image Processing Laboratory Beihang University Beijing China

The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view gait. Gait Energy image (GEI) is used to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x; y; view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs the best.

关键词： Cameras Tensile stress Biometrics Surveillance image analysis image recognition Rendering (computer graphics) Principal component analysis Feature extraction Voting

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Wavelet-based enhancement of color image using combined local variance and entropy analysis

Wavelet-based enhancement of color image using combined loca...

引用

IEEE International Conference on Consumer Electronics (ICCE)

作者： Sinyoung Jun Eunsung Lee Sangjin Kim Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea

ISBN: (纸本)9781424443147

关键词： Wavelet analysis Entropy Analysis of variance image analysis image color analysis Noise reduction Colored noise image enhancement Cameras Wavelet transforms

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image restoration using alpha map-based fusion for multiple color aperture computational camera

Image restoration using alpha map-based fusion for multiple ...

引用

IEEE International Conference on Consumer Electronics (ICCE)

作者： Eunsung Lee Vivek Maik Sinyoung Jun Sangjin Kim Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea

ISBN: (纸本)9781424443147;9781424443161

关键词： image restoration Apertures Focusing Signal processing algorithms Color image fusion Smart cameras Costs Digital images Educational technology

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Age Classification Base on Gait Using HMM

Age Classification Base on Gait Using HMM

引用

International Conference on Pattern Recognition

作者： De Zhang Yunhong Wang Bir Bhanu Intelligent Recognition and Image Processing Laboratory School of Computer Science and Engineering Beihang University Beijing China Center of Research in Intelligent Systems University of California Riverside CA USA

关键词： Hidden Markov models Senior citizens Databases Legged locomotion Feature extraction Humans Pixel

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