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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Hierarchical Extreme Learning Machine for Unsupervised Representation Learning

Hierarchical Extreme Learning Machine for Unsupervised Repre...

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International Joint Conference on Neural Networks

作者： Wentao Zhu Jun Miao Laiyun Qing Guang-Bin Huang Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China School of Electrical & Electronic Engineering Nanyang Technological University Nanyang Avenue Singapore 639798

ISBN: (纸本)9781479919611

Learning representations from massive unlabeled data is a hot topic for high-level tasks in many applications. The recent great improvements on benchmark data sets, which are achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters, usually require many tedious tricks and much expertise to tune. However, filters learned by these complex architectures are quite similar to standard hand-crafted features visually, and training the deep models costs quite long time to fine-tune their weights. In this paper, Extreme Learning Machine-Autoencoder (ELM-AE) is employed as the learning unit to learn local receptive fields at each layer, and the lower layer responses are transferred to the last layer (trans-layer) to form a more complete representation to retain more information. In addition, some beneficial methods in deep learning architectures such as local contrast normalization and whitening are added to the proposed hierarchical Extreme Learning Machine networks to further boost the performance. The obtained trans-layer representations are followed by block histograms with binary hashing to learn translation and rotation invariant representations, which are utilized to do high-level tasks such as recognition and detection. Compared to traditional deep learning methods, the proposed trans-layer representation method with ELM-AE based learning of local receptive filters has much faster learning speed and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101. State-of-the-art performances are achieved on both Caltech 101 15 samples per class task and 4 of 6 MNIST variations data sets, and highly impressive results are obtained on MNIST data set and other tasks.

关键词： Hierarchical Extreme Learning Machine Unsupervised Representation Learning deep learning models Learning machines Dataset representation method Recognition (Psychology) Receptive field Learning representations Religious Missions Hierarchical

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A parallel scheme for three-dimensional reconstruction in large-field electron tomography

A parallel scheme for three-dimensional reconstruction in la...

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10th International Symposium on Bioinformatics Research and Applications, ISBRA 2014

作者： Zhang, Jingrong Wan, Xiaohua Zhang, Fa Ren, Fei Wang, Xuan Liu, Zhiyong Key Lab. of Intelligent Information Processing and Advanced Computing Research Lab Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Yanshan University Yanshan China

ISBN: (纸本)9783319081700

Large-field high-resolution electron tomography enables visualizing detailed mechanisms under global structure. As field enlarges, the processing time increases and the distortions in reconstruction become more critical. Adopting a nonlinear projection model instead of a linear one can compensate for curvilinear trajectories, nonlinear electron optics and sample warping. But the processing time for the reconstruction with nonlinear projection model is rather considerable. In this work, we propose a new parallel strategy for block iterative reconstruction algorithms. We also adopt a page-based data transfer in this strategy so as to dramatically reduce the processing time for data transfer and communication. We have tested this parallel strategy and it can yield speedups of approximate 40 times according to our experimental results. © 2014 Springer International Publishing Switzerland.

关键词： Iterative methods

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Fusion of polarization image based on curvelet transform

Fusion of polarization image based on curvelet transform

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2014 2nd International Conference on Mechatronics, Robotics and Automation, ICMRA 2014

作者： Zhang, De Xiang Wang, Hong Hai Xue, Feng The Department of Information and Communication Technology Anhui Sanlian University Hefei 230601 China Key Lab. of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China

ISBN: (纸本)9783038350781

Curvelet transform is the combination of the multi-scale analysis and multi-directional analysis transforms, which is more suitable for objects with curves. Applications of the curvelet transform have increased rapidly in the field of image fusion. Firstly, using the curvelet transform, several polarization images can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of region variance information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused curvelet coefficients. In the present work an algorithm for image fusion based on the curvelet transform was implemented, analyzed, and compared with a wavelet-based fusion algorithm. Experimental results show that the proposed algorithm works better in preserving the edges and texture information compared with the wavelet-based image fusion algorithms. © (2014) Trans Tech Publications, Switzerland.

关键词： Image fusion

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Artifact reduction of compressed video via three-dimensional adaptive estimation of transform coefficients

Artifact reduction of compressed video via three-dimensional...

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作者： Zhang, Xinfeng Xiong, Ruiqin Ma, Siwei Gao, Wen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institute of Digital Media Peking University Beijing100871 China

ISBN: (纸本)9781479957514

Block transform compressed videos usually suffer from annoying artifacts at low bit rates, caused by the coarse quantization of transform coefficients. The inter prediction utilized in video coding also induces block boundary artifacts when the neighboring blocks using different motion vectors from previous decoded frames. In this paper, a three-dimensional adaptive estimation method for video transform coefficients is proposed to reduce the artifact in compressed video. In the proposed method, transform coefficients of each block are estimated by adaptively fusing three prediction sources based on their reliabilities. One prediction source is the transform coefficients directly acquired from the decoded video, whose reliability is determined by the distribution of the quantization noise. The other prediction source is derived by a temporal autoregressive model of transform-blocks along motion trajectory among neighboring reference frames. Its reliability is estimated from prediction variance of local blocks. The last prediction source is derived from the nonlocal transform-blocks, whose reliability is estimated based on the distribution of the nonlocal coefficients and their similarity with the estimated block. Experimental results for compressed video sequences by HEVC show that, the proposed method can reduce the compression artifacts and improve both the objective and subjective quality. © 2014 IEEE.

关键词： Forecasting

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An improved correlation method based on rotation invariant feature for automatic particle selection

An improved correlation method based on rotation invariant f...

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10th International Symposium on Bioinformatics Research and Applications, ISBRA 2014

作者： Chen, Yu Ren, Fei Wan, Xiaohua Wang, Xuan Zhang, Fa Key Lab. of Intelligent Information Processing and Advanced Computing Research Lab Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing Beijing China Yanshan University Yanshan China

ISBN: (纸本)9783319081700

Particle selection from cryo-electron microscopy (cryo-EM) images is very important for high-resolution reconstruction of macromolecular structure. However, the accuracy of existing selection methods are normally restricted to noise and low contrast of cryo-EM images. In this paper, we presented an improved correlation method based on rotation invariant features for automatic, fast particle selection. We first selected a preliminary particle set applying rotation invariant features, then filtered the preliminary particle set using correlation to reduce the interference of high noise background and improve the precision of correlation method. We used Divide and Conquer technique and cascade strategy to improve the recognition ability of features and reduce processing time. Experimental results on the benchmark of cryo-EM images show that our method can improve the accuracy of particle selection significantly. © 2014 Springer International Publishing Switzerland.

关键词： Correlation methods

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Signal processing for the 5G Revolution

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IEEE SIGNAL processing MAGAZINE 2014年第6期31卷 12-13页

作者： Heath, Robert W., Jr. Leus, Geert Quek, Tony Q. S. Talwar, Shilpa Zhou, Peiying Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Department of Computer Systems University of São Paulo - USP São Carlos Brazil IRISA Research Institute University of South Brittany - IRISA/UBS Vannes France

The articles in this special section provide an overview of recent advances in signal processing for communication with an emphasis on signal processing techniques that will be relevant for 5G cellular systems. It covers a wide range of topics including modulation, beamforming, cross-layer optimization based on different performance metrics, location-aware communication, cloud computing, and cloud radio access networks. The articles provide a diverse perspective on the potential challenges in 5G cellular systems.

关键词： Special issues and sections 5G mobile communication Next generation networking Wireless communication Cellular networks Wireless cellular networks

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A representative local region detector based on color-contrast-MSER 14

A representative local region detector based on color-contra...

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2014 4th ACM International Conference on Multimedia Retrieval, ICMR 2014

作者： Cao, Yang Gao, Ke Tang, Sheng Zhang, Yongdong Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)1595930361

In order to extract representative local invariant regions in textured natural images, we propose a Color-Contrast-MSER (CCM) detector with color-contrast pixel ranking, which can reduce the number of meaningless regions extracted from backgrounds. The main contributions are threefold: (1) In contrast with the original MSER[3] which adopts intensity pixel ranking, we develop a new pixel ranking mechanism based on color contrast analysis. (2) In this paper, the pixel ranking value of each pixel is defined as the color contrast between a kernel-sized window and the background. Therefore we propose an adaptive background scale selection mechanism that simulates the background color distribution as the benchmark for color contrast. (3) The experimental results demonstrate that compared with the original MSER detector[3], our Color-Contrast-MSER (CCM) detector can extract more representative local regions with competitive repeatability score at only 50% computational time and 10% memory cost. Copyright 2014 ACM.

关键词： Pixels

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Collecting valuable information from fast text streams 8

Collecting valuable information from fast text streams

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8th IFIP International Conference on intelligent information processing, IIP 2014

作者： Qi, Baoyuan Ma, Gang Shi, Zhongzhi Wang, Wei Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Beijing Lexo Technologies Co. Ltd Beijing100080 China

ISBN: (纸本)9783662449790

It has become a challenging work to collect valuable information from fast text streams. In this work, we propose a method which gains useful information effectively and efficiently. Firstly, we maintain an analyzer based on the Trie structure and the dynamic N-Gram tokenizer;secondly, unlike the traditional search engine principle, we consider the documents as a query by building the indexes for the whole query base. The experimental results show that it has the strong adaption ability, low latency and high quality support for the complex query combination compared with the conventional methods. © IFIP International Federation for information processing 2014.

关键词： Search engines

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Representative local features mining for large-scale near-duplicates retrieval

Representative local features mining for large-scale near-du...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoguang Gu Yongdong Zhang Dongming Zhang Jintao Li Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Local features have been widely used in many computer vision related researches, such as near-duplicate image and video retrieval. However, the storage and query cost of local features become prohibitive on large-scale database. In this paper, we propose a representative local features mining method to generate a compact but more effective feature subset. First, we do an unsupervised annotation for all similar images(or frames in video) in the database. Second, we compute a comprehensive score for every local feature. The score function combines the robustness and discrimination. Finally, we sort all the local features in an image by their scores and the low-score local features can be removed. The selected local features are robust and discriminative, which can guarantee the better retrieval quality than using full of the original feature set. By our method, the number of local features can be significantly reduced and a large amount of storage and computational cost can be saved. The experimental results show that we can use 30% of the features to get a better query performance than that of full feature set.

关键词： Databases Robustness Vectors Visualization Feature extraction Binary codes Complexity theory

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