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IEEE International Symposium on Biomedical Imaging

作者： Kangjoo Lee Sungho Tak Jong Chul Ye Bio-Imaging & Signal Processing Laboratory Department Bio & Brain Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (纸本)9781424441273

A novel data-driven sparse generalized linear model (GLM) and statistical analysis method for fMRI is developed. Although independent component analysis (ICA) has been broadly applied to fMRI to separate spatially or temporally independent components, recent studies show that ICA does not guarantee independence of simultaneously occurred distinct activity patterns in the brain and sparsity of the signal has been shown to be more important. Motivated from the ICA and biological findings such as sparse coding in the primary visual cortex simple cells, we propose a compressed sensing based data-driven sparse GLM solely based upon the sparsity of the signal. It enables estimation of spatially adaptive design matrix from sparse signal components that represent synchronous neural hemodynamics. Furthermore, an MDL based model order selection rule can determine unknown sparsity for sparse dictionary learning.

关键词： Dictionaries Sparse matrices Encoding Algorithm design and analysis Correlation Independent component analysis Brain modeling

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Towards audio-video based handwritten mathematical content recognition in classroom videos

Towards audio-video based handwritten mathematical content r...

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IEEE Pacific Rim Conference on Communications, Computers and signal processing

作者： Smita Vemulapalli Monson Hayes Center for Signal & Image Processing Georgia Institute of Technology Atlanta GA USA Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea

Recognizing handwritten mathematical content in classroom videos poses a range of interesting challenges. In this paper, we focus on improving the character recognition accuracy in such videos using a combination of video and audio based text recognizers. We propose a two step assembly consisting of a video text recognizer (VTR) as the primary character recognizer and an audio text recognizer (ATR) for disambiguating, if needed, the output of the VTR. We propose techniques for (1) detecting ambiguity in the output of the VTR so that a combination with the ATR may be triggered only for ambiguous characters, (2) synchronizing the output of the two recognizers for enabling combination, and (3) combining the options generated by the two recognizers using measurement and rank based methods. We have implemented the system using an open source implementation of a character recognizer and a commercially available phonetic word-spotter. Through experiments conducted using video recorded in a classroom-like environment, we demonstrate the improvement in the character recognition accuracy that can be achieved using our approach.

关键词： Videos Character recognition Accuracy Video recording Synchronization Text recognition Handwriting recognition

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Synchronization and combination techniques for audio-video based handwritten mathematical content recognition in classroom videos

Synchronization and combination techniques for audio-video b...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Smita Vemulapalli Monson Hayes Center of Signal & Image Processing Georgia Institute of Technology Atlanta GA USA Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea

Recognizing handwritten mathematical content is a challenging problem, and more so when such content appears in classroom videos. However, given the fact that in such videos the handwritten text and the accompanying audio refer to the same content, a combination of a video and an audio based recognizer has the potential to significantly improve the content recognition accuracy. In this paper, using a combination of video and audio based recognizers, we focus on improving the character recognition accuracy in such videos and propose: (1) synchronization techniques for establishing a correspondence between the handwritten and the spoken content, and (2) combination techniques for combining the outputs of the video and audio based recognizers. The current implementation of the system makes use of a modified open source text recognizer and a commercially available phonetic word-spotter. For evaluation purposes, we use videos recorded in a classroom-like environment and our experiments demonstrate the significant improvements (≈ 24% relative increase as compared to the baseline video based recognizer) in character recognition accuracy that can be achieved using our techniques.

关键词： Videos Synchronization Character recognition Accuracy Video recording Handwriting recognition Speech recognition

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L0-compressed sensing for parallel dynamic MRI using sparse Bayesian learning

L0-compressed sensing for parallel dynamic MRI using sparse ...

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IEEE International Symposium on Biomedical Imaging

作者： Hong Jung HuiSu Yoon Jong Chul Ye Bio-Imaging & Signal Processing Laboratory Department of Bio and Brain engineering Korea Advanced Institute of Science and Technology Daejeon South Korea

Since the advent of compressed sensing in dynamic MR imaging area, a number of l 1 -compressed sensing algorithms have been proposed to improve the resolution. Recently, it was shown that by solving an l p minimizati... 详细信息

ISBN: (纸本)9781424441273

关键词： Minimization Coils Magnetic resonance imaging image reconstruction Compressed sensing Bayesian methods

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Traffic speed measurement using Spatio-Temporal model and frequency domain analysis

Traffic speed measurement using Spatio-Temporal model and fr...

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information technology, ECTI-CON

作者： Arucha Rungchokanun Vutipong Areekul Supakorn Siddhichai Hiroaki Kunieda Kasetsart Signal & Image Processing Laboratory (KSIP Lab) Department of Electrical Engineering Kasetsart University Thailand Image Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Department of Communication and Integrated Systems Tokyo Institute of Technology Meguro Tokyo Japan

In traffic monitoring applications, traffic speed is an important parameter of traffic management. The method for traffic speed measurement using video based on Spatio-Temporal (ST) model and frequency domain analysis is proposed in this paper. This method is designed to be able to measure the traffic speed in every pattern of road. The novel of proposed method is unfolding the edge information on ST model and analyse them in frequency domain to determine traffic speed. The proposed method is evaluated by compare with actual speed. The traffic speed is measured accurately 97.1%.

关键词： Analytical models Sensors Pixel image sensors Monitoring

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Feature extraction for cancer prediction by tensor decomposition of 1D protein expression levels

Feature extraction for cancer prediction by tensor decomposi...

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2nd International Conference on Computational Bioscience, CompBio 2011

作者： Kopriva, Ivica Jukić, Ante Cichocki, Andrzej Division of Laser and Atomic Research and Development Rudler Bošković Institute Bijenička cesta 54 10002 Zagreb Croatia Laboratory for Advanced Brain Signal Processing Brain Science Institute RIKEN 2-1Hirosawa Wako-shi Saitama 351-0198 Japan Warsaw University of Technology Systems Research Institute PAN Poland

ISBN: (纸本)9780889868892

Tensor decomposition approach to feature extraction from one-dimensional data samples is presented. One-dimensional data samples are transformed into matrices of appropriate dimensions that are further concatenated into a third order tensor. This tensor is factorized according to the Tucker-2 model by means of the higher-order-orthogonal iteration (HOOI) algorithm. Derived method is validated on publicly available and well known datasets comprised of low-resolution mass spectra of cancerous and non-cancerous samples related to ovarian and prostate cancers. The method respectively achieved, in 200 independent two-fold cross-validations, average sensitivity of 96.8% (sd 2.9%) and 99.6% (sd 1.2%) and average specificity of 95.4% (sd 3.5%) and 98.7% (sd 2.9%). Due to the widespread significance of mass spectrometry for monitoring protein expression levels and cancer prediction it is conjectured that presented feature extraction scheme can be of practical importance.

关键词： Feature extraction

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COSPARSE ANALYSIS MODELING - UNIQUENESS AND ALGORITHMS

COSPARSE ANALYSIS MODELING - UNIQUENESS AND ALGORITHMS

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Sangnam Nam Michael E. Davies Michael Elad Remi Gribonval IRISA INRIA Rennes Bretagne Atlantique France IDCOM & Joint Research Institute of Signal and Image Processing Edinburgh University UK Department of Computer Science Technion-Israel Institute of Technology Israel

In the past decade there has been a great interest in a synthesis-based model for signals, based on sparse and redundant representations. Such a model assumes that the signal of interest can be composed as a linear combination of few columns from a given matrix (the dictionary). An alternative analysis-based model can be envisioned, where an analysis operator multiplies the signal, leading to a cosparse outcome. In this paper, we consider this analysis model, in the context of a generic missing data problem (e.g., compressed sensing, inpainting, source separation, etc.). Our work proposes a uniqueness result for the solution of this problem, based on properties of the analysis operator and the measurement matrix. This paper also considers two pursuit algorithms for solving the missing data problem, an L1-based and a new greedy method. Our simulations demonstrate the appeal of the analysis model, and the success of the pursuit techniques presented.

关键词： Analytical models Data models Algorithm design and analysis Matching pursuit algorithms Optimization Dictionaries Computational modeling

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MST based stream data clustering algorithm

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2011年第9期5卷 352-360页

作者： Cheng, Zheng Xiaodong, Zhu Jingcheng, Li Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Anhui University China School of Computer Science and Technology Anhui University China Institute of Information Management and Electronic Business University of Shanghai for Science and Technology China

How to make use of limited memory space and processing speeds of computer for rapid and accurate data mining has become an important research topic on the stream data cluster analysis. A stream data clustering algorithm based on the minimum spanning tree (MSTSC) is described. MSTSC is divided into online processing and offline clustering. Stream data are analyzed online by using two groups of processing unit respectively. In offline process clusters is taken as representative objects, and the minimum spanning tree algorithm is used in offline clustering. MSTSC can improve the clustering quality on non-spherical clusters. Some experiments are carried out in both real data sets and synthetic data sets. Results show that MSTSC algorithm not only can deal with non-spherical clusters effectively, but also has better efficiency and clustering quality. In addition, MSTSC is insensitive to order of input data, and has a good effect for skewed class distributions.

关键词： Cluster analysis

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Finding protein active sites using approximate sub-graph isomorphism

Finding protein active sites using approximate sub-graph iso...

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Middle East Conference on Biomedical Engineering (MECBME)

作者： Mohammad Ebrahim Abbasi Dezfouli Seyed Shahriar Arab Emad Fatemizadeh Najmeh Hosseynimanesh Electrical Engineering Department BiSIPL (Biomedical Image and Signal Processing Lab) Sharif University of Technology Tehran Iran School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran

Prediction of the amino acids that have a catalytic effect on the enzymes is a major stage in appointing the activity of the enzymes and classification. The biological activity of a protein usually depends on the existence of a small number of amino acids. Recently, many algorithms have been proposed in the literature for finding these amino acids which are complex and time consuming. In this paper, we will introduce a new method for predicting the active sites that will use the spatial coordinates and the type of amino acids that contain the active sites. In order to increase the speed we use an approximate graph isomorphism algorithm. Furthermore, this algorithm allows us to find several active sites for a protein and order them according to a RMSD (Root Mean Square Deviation) number which has several applications in biology.

关键词： Proteins Amino acids Approximation algorithms Protein engineering Classification algorithms Databases

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Recognizing Cursive Arabic Text:Using statistical features and interconnected mono-HMMs

Recognizing Cursive Arabic Text:Using statistical features a...

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2011 4th International Congress on image and signal processing(第四届图像与信号处理国际学术会议 CISP 2011)

作者： M S Khorsheed H Al-Omari Image Processing and Signal Analysis & Recognition (IPSAR) Research Group Computer Research Institute King Abdulaziz City for Science and Technology PO Box 6086 Riyadh 11442 Saudi Arabia

This paper presents a cursive Arabic text recognition system. The system decomposes the document image into test line images and extracts a set of simple statistical features from a one-pixel width window which is sliding a cross that text line. It then injects the resulting feature vectors to Hidden Markov Models. The proposed system is applied to a data corpus which includes Arabic text of more than 600 A4-size sheets typewritten in multiple computergenerated fonts.

关键词： Document analysis pattern recognition Arabic text recognition hidden Markov models HTK

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