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检索条件"机构=Science and Technology on Blind Signal Processing Laboratory"

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Volume-based Magnetic Resonance Brain Image Classification

Volume-based Magnetic Resonance Brain Image Classification

引用

International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Yaw-Jiunn Chiou Jyh-Wen Chai Clayton Chi-Chang Chen Shih-Yu Chen Hsian-Min Chen Yen-Chieh Ouyang Wu-Chung Su Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices.

关键词： Training Support vector machine classification Image classification Vectors Volume measurement Magnetic resonance

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Techniques for Automatic Magnetic Resonance Image Classification

Techniques for Automatic Magnetic Resonance Image Classifica...

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Hsian-Min Chen Shih-Yu Chen Jyh Wen Chai Clayton Chi-Chang Chen Chao-Cheng Wu Yen-Chieh Ouyang Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions.

关键词： Classification algorithms Training Image segmentation Brain Skull Software algorithms Support vector machines

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Surveillance of pedestrian bridge traffic using neural networks

Surveillance of pedestrian bridge traffic using neural netwo...

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Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2009

作者： Watkins, Steve E. Stanley, R. Joe Gopal, Anand Moss, Randy H. Department of Electrical and Computer Engineering Applied Optics Laboratory Missouri University of Science and Technology Rolla MO 65409 United States Department of Electrical and Computer Engineering Signal Processing Laboratory Missouri University of Science and Technology Rolla MO 65409 United States

ISBN: (纸本)9780819475527

A computer-vision monitoring system is demonstrated that automatically detects the presence and location of people. The approach investigated the potential for real-time, automated surveillance and tracking in a realistic environment. Economy was obtained by the use of gray-scale, fixed perspective images and efficiency was obtained by the use of selected object features and a neural-network-processing algorithm. The system was applied to pedestrian traffic on an outdoor bridge and consequently had to handle complex images. Image sequences of single and multiple people were used with differences in clothing, position, lighting, season, etc. A two-stage algorithm was implemented in which (1) new objects were identified in a highly variable scene and (2) the objects were classified with a back-propagation neural network. The image processing techniques included segmentation and filtering and the neural network used fourteen object features as inputs. The implementation had excellent people-discrimination accuracy despite the noise in the images and had low computational complexity with respect to alternative techniques. © 2009 SPIE.

关键词： Neural networks

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Despeckling SAR images using bayes-shrink in bandelet domain

Despeckling SAR images using bayes-shrink in bandelet domain

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2009 2nd International Congress on Image and signal processing, CISP'09

作者： Yanan, Xu Qingwei, Gao Yixiang, Lu Weinian, Zhong Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China Institute of Electronic Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781424441310

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of scattering phenomena. This paper presents a despeckling method for SAR images based on adaptive bandelets thresholding. This threshold is derived in a Bayesian framework. The proposed threshold is simple and closed form, and it is applied to adaptive bandelets coefficients to achieve more satisfying results. The performances of adaptive bandelets Bayesshrink soft-thresholding and wavelet thresholding for despeckling SAR images are compared through an experiment. Experiment results clearly demonstrated the capability of the proposed scheme in SAR image speckle reduction especially for SAR images possessing detailed textures. ©2009 IEEE.

关键词： Synthetic aperture radar

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Spectral Analysis for Emotion Recognition by NMF Features

Spectral Analysis for Emotion Recognition by NMF Features

引用

International Conference on Natural Computation (ICNC)

作者： Kyungjoong Jeong Jaiyoun Song Hong Jeong Advanced Signal Processing Laboratory Pohang University of Science and Technology Pohang South Korea

ISBN: (纸本)9781424445424

In this paper, we propose a new algorithm, which is using NMF features and which is different from traditional algorithms using temporal variations of the spectrogram. The proposed algorithm has been tested in several different ways by varying NMF and the speech database. The experimental results show performance of 90% and classification success of 77% for speaker dependent and independent cases, respectively.

关键词： Spectral analysis Emotion recognition signal processing algorithms Spectrogram Testing Spatial databases Feature extraction Speech processing Speech analysis Laboratories

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Arithmetic extended-mapping for BICM-ID with repetition codes

Arithmetic extended-mapping for BICM-ID with repetition code...

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International ITG Workshop on Smart Antennas, WSA 2009

作者： Yano, Takashi Matsumoto, Tad Central Research Laboratory Hitachi Ltd 1-280 Higashi-koigakubo Kokubunji Tokyo185-8601 Japan Information Theory and Signal Processing Laboratory Japan Advanced Institute of Science and Technology University of Oulu 1-1 Asahidai Nomi Ishikawa923-1292 Finland

A novel arithmetic extended mapping technique is proposed in this paper for bit-interleaved coded modulation with iterative decoding (BICM-ID) with repetition codes as well as for their modifications. With the proposed mapping scheme, the computational complexity of BICM-ID is significantly reduced compared with conventional quadrature amplitude modulation (QAM) mapping schemes Performances of BICM-ID using the proposed extended mapping rules are also verified both by the extrinsic information transfer (EXIT) analysis and bit error rate (BER) simulations. The results show that with the proposed BICM-ID scheme, the code rate, at which, given the channel signal-to-noise power ratio (SNR) bit error rate can made arbitrarily small, is almost 90% of channel capacity, and such excellent property holds at relatively wide range of SNR values.

关键词： Mapping

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Identifying protein-protein interaction sites using adapted Bayesian classifier

Identifying protein-protein interaction sites using adapted ...

引用

2009 Second ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM 2009

作者： Chishe, Wang Jie, Song Fangping, Li Junsong, Lv School of Information Technology Jinling Institute of Technology Nanjing 211169 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China Center of Network University of Science and Technology of China Hefei 230029 China Anhui Statistics Bureau Hefei 230001 China

ISBN: (纸本)9781424442461

Identifying protein-protein interaction sites have important connotations ranging from rational drug design to analysis metabolic and signal transduction networks. In this paper, we presented an adapted Bayesian classifier based on tree augmented naïve Bayesian classifier to predict interface residues of protein-protein interaction sites. This classifier used fixed structure which could denote the correlation of sequence neighborhood and the character of protein's features. The conditional probability tables of the classifier were learned from the training dataset. By testing our approach on 81 hetero-complex chains, experimental results demonstrate the performance of our approach is indeed superior to current existing methods. The success of the predictions is validated by examining the predictions in the context of the three-dimensional structures of protein complexes. ©2009 IEEE.

关键词： Forecasting

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Accurate 3D shape estimation based on combinatorial optimization

Accurate 3D shape estimation based on combinatorial optimiza...

引用

IEEE International Conference on Image processing

作者： Seong-O Shim Tae-Sun Choi Signal and Image Processing Laboratory School of Information and Mechatronics Gwangju Institute of Science and Technology Gyeongju South Korea

The problem of estimating the three-dimensional (3D) geometry of an object from a sequence of images obtained at different focus settings is called shape from focus (SFF). The conventional SFF methods apply focus measure operator at each pixel using neighboring pixels in the same image frame. However, for an object with complex geometry, such methods cannot compute accurate focus level of a pixel, since the neighboring pixels in an image, taken with small depth of field, do not have the same focus level. In this paper, a novel SFF algorithm based on combinatorial optimization is proposed. After the rough estimate of the shape, we refine the shape iteratively by searching the optimal focus measure of each pixel using neighboring pixels on various image frames. The proposed SFF algorithm shows improvements in both the accuracy of the shape and the computational complexity in comparison to the previous SFF methods.

关键词： Focusing Pixel Shape measurement Iterative algorithms Computational complexity Layout Volume measurement signal processing Image processing Mechatronics

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Approximating 3D shape using Bezier surface

Approximating 3D shape using Bezier surface

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Mannan Saeed Muhammad Muhammad Tariq Mahamood Tae-Sun Choi Signal and Image Processing Laboratory School of Information and Mechatronics Gwangju Institute of Science and Technology Gwangju South Korea

Estimating 3D shape of the object is an important research topic in the area of computer vision, with a wide range of applications. This paper introduces a new method for focused-based passive methods (like SFF) to approximate the 3D shape of the object using Bezier surface. The discrete nature of image sampling results in the loss of information between two consecutive images. Conventional approximation methods optimize or interpolate focus values. We have suggested interpolating depth values instead of focus value over a small patch on the object surface. The method approximates the surface more accurately and also reduces any noise caused by focus measure. The proposed method is tested and analyzed to demonstrate the effectiveness against traditional methods.

关键词： Shape Focusing Computer vision Application software Image sampling Approximation methods Optimization methods Noise reduction Noise measurement Testing

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A 5-Parameter Bundle Adjustment Method for Image Mosaic

A 5-Parameter Bundle Adjustment Method for Image Mosaic

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International Conference on Computer and Information science (ACIS)

作者： Xianyong Fang Bin Luo Haifeng Zhao Yiwen Zhang Key Laboratory of Intelligent Computing and Signal Processing of MOE School of Computer Science and Technology Anhui University China

A 5-parameter bundle adjustment method is proposed in this paper for global mosaic of an image sequence. By decomposing the rotation matrix into a 3-parameter rotation axis and a rotation angle, to each image, there are 5 parameters that need estimate for rotation axis, rotation angle and focal length. The proposed method minimizes the distance between feature matches in 3D space so that these 5 parameters are refined gradually for final stitching of all images together. Experimental results suggest that this method is an useful extension to existing research with the advantages of real 3D camera motion, accurate 5-parameter rotation decomposition and reliable 3D feature definition.

关键词： Image sequences Matrix decomposition Parameter estimation Cameras

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