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Picture Coding Symposium, PCS

作者： Marc Bosch Fengqing Zhu Edward J. Delp Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In recent years there has been a growing interest in developing novel techniques for increasing the coding efficiency of video compression methods. We approach the problem by not encoding all the pixels, in particular, regions belonging to areas that the viewer will not perceive the specific details in the scene could be skipped or encoded at a much lower data rate. This approach can also be expanded by considering a model of the human visual perception system. In this paper we review some of the approaches we have investigated at Purdue University. The goal is to determine where ldquodetail-irrelevantrdquo regions in the frame are located and not encode them. We will also discuss a set of subjective quality evaluation experiments to determine what is the overall perceptual quality of these approaches.

关键词： video coding image coding Humans image reconstruction Decoding Layout Synthesizers video sequences video compression Motion analysis

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Segmentation and registration based analysis of microscopy images

Segmentation and registration based analysis of microscopy i...

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IEEE International Conference on image processing

作者： Kevin S. Lorenz Francisco Serrano Paul Salama Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN USA School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA

Optical microscopy exhibits many challenges for digital image analysis. In general, microscopy volumes are inherently anisotropic, suffer from decreasing contrast with tissue depth, and characteristically have low signal levels. This paper describes a method to segment multiphoton fluorescent microscopy images via a combination of segmentation and registration methods. In particular, the proposed method utilizes image enhancement and spatial filtering along with registration and temporal filtering. Experimental results demonstrate the efficacy of the proposed method.

关键词： image segmentation image analysis Optical microscopy Filtering Geometrical optics Optical filters Digital images Anisotropic magnetoresistance Fluorescence image enhancement

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Fetal R-wave detection from multichannel abdominal ECG recordings in low SNR

Fetal R-wave detection from multichannel abdominal ECG recor...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shahrzad Kharabian Mohammad B. Shamsollahi Reza Sameni Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran School of Electrical & Computer Engineering Shiraz University Shiraz Iran

Abdominal recordings of fetal ECG (fECG) have lower signal-to-noise ratio (SNR) as compared with invasive procedures. In this paper we have combined two previously proposed methods, one for extracting fECG, called piCA and the other, a transformation based on Hilbert transform to enhance the R-peaks. The combination of these methods seems to work well in situations of noisy data and fetal repositioning. Also a comparison is done by using ICA in order to extract the fetal signals. Performance of both methods is studied separately. Results show that applying the transformation on the components extracted with the use of piCA (after maternal ECG cancellation), had a very good performance. Also, as piCA ranks the components according to their resemblance to maternal ECG, it could be used in automatic fetal ECG extraction algorithms.

关键词： Abdomen Electrocardiography Signal processing algorithms Fetal heart Data mining Independent component analysis Magnetic recording Interference Magnetic separation Electrodes

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Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines

Gait Recognition Using Wavelet Packet Silhouette Representat...

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Congress on image and Signal processing, CISP

作者： Farzin Dadashi Babak N. Araabi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA

Gait is an idiosyncratic biometric that can be used for human identification at a distance and as a result gained growing interest in intelligent visual surveillance. In this paper, an efficient gait recognition method based on describing subject outer body contour deformations using wavelet packets is proposed. With the use of matching pursuit algorithm, k bases of wavelet packet tree that have maximum similarity to the signal are selected and corresponding coefficients are used as features. Finally, transductive support vector machine (TSVM) classification is utilized on computed eigengait space for semi-supervised identification. The proposed method of selecting features which uses a complete orthogonal or near orthogonal basis from a wavelet packet library of bases and investigating the correlational structure of gait features for each individual using TSVM, result in encouraging identification performance.

关键词： Wavelet packets Support vector machines Matching pursuit algorithms Biometrics Humans Machine intelligence Surveillance Pursuit algorithms Support vector machine classification Libraries

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Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery

Component analysis-based unsupervised linear spectral mixtur...

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Workshop on Hyperspectral image and Signal processing: Evolution in Remote Sensing, WHISPERS

作者： Xiaoli Jiao Yingzi Du Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science & Electrical Engineering University of Maryland Baltimore MD USA Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

One of the most challenging issues in unsupervised linear spectral mixture analysis (LSMA) is how to obtain unknown knowledge of target signatures referred to as virtual endmembers (VEs) directly from the data to be processed. This issue has never arisen in supervised LSMA where the VEs are either assumed to be known a priori or can be provided by visual inspection. With the recent advent of hyperspectral sensor technology many unknown and subtle signal sources can be uncovered and revealed without prior knowledge. This paper addresses this issue and develops a component analysis-based unsupervised LSMA where the desired VEs can be extracted by component analysis-based transforms directly from the data to be processed without appealing for prior knowledge. In order to substantiate the utility of the proposed approach extensive experiments are conducted for demonstration.

关键词： image analysis Spectral analysis Hyperspectral imaging Independent component analysis Personal communication networks Inspection Statistics Signal analysis Hyperspectral sensors Principal component analysis

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Brain tissue classification using independent vector analysis (IVA) for magnetic resonance image

Brain tissue classification using independent vector analysi...

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2009 9th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2009

作者： Chiou, Yaw-Jiunn Chen, Hsiang-Min Chai, Jyh Wen Chen, Clayton Chi-Chang Ouyang, Yen-Chieh Su, Wu-Chung Yang, Ching-Wen Lee, San-Kan Chang, Chein-I. Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Computer Center Taichung Veterans General Hospital Taichung 407 Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769536569

The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach. The proposed method can resolve the permutation problem during individual ICA runs for group brain MR images. The proposed IVA method in conjunction with support vector machine (SVM), we can effectively separate the different part of gray, white matter and cerebrospinal fluid (CSF) from brain soft tissues. In order to demonstrate the proposed IVA-SVM method, experiments are conducted for performance analysis and evaluation. Simulation results show that using IVA can greatly release from the problem cause from traditional ICA to the situation of analyzing inconsistent results of MR image. © 2009 IEEE.

关键词： Magnetic resonance imaging

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An automatic computer-aided detection system for meniscal tears on magnetic resonance images

引用

IEEE Transactions on Medical Imaging 2009年第8期28卷 1308-1316页

作者： Ramakrishna, Bharath Liu, Weimin Saiprasad, Ganesh Safdar, Nabile Chang, Chein-I Siddiqui, Khan Kim, W. Siegel, Eliot Chai, Jyh-Wen Chen, Clayton Chi-Chang Lee, San-Kan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD 21250 United States University of Maryland School of Medicine Baltimore MD 21201 United States Department of Electrical Engineering and Environmental Restoration Disaster Reduction Research Center National Chung Hsing University Taichung 402 Taiwan VA Maryland Health Care System Baltimore MD 21201 United States Hospital of the University of Pennsylvania Philadelphia PA 19104 United States Department of Radiology College of Medicine China Medical University Taichung 404 Taiwan Department of Radiology Taichung Veterans General Hospital Taichung 40705 Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Department of Radiology Taichung Veterans General Hospital Taichung 407 Taiwan

Knee-related injuries including meniscal tears are common in both young athletes and the aging population, and require accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists' experienced skills, confidence in detection of meniscal tears can be quite high. This paper develops a novel computer-aided detection (CAD) diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and specificity of 77.41% and 81.39%, respectively, obtained by experienced radiologists in routine diagnosis without using the CAD. The experimental results suggest that the developed CAD system has great potential and promise in automatic detection of both simple and complex meniscal tears of the knee. © 2009 IEEE.

关键词： Magnetic resonance imaging

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An architecture for multimedia analysis and retrieval based on fuzzy description logics

An architecture for multimedia analysis and retrieval based ...

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2nd K-Space PhD Jamboree Workshop, PhDJamboree 2008

作者： Simou, Nikolaos Athanasiadis, Thanos Kollias, Stefanos Image Video and Multimedia Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

Effective management and exploitation of multimedia documents requires extraction of the underlying semantics. In this paper we propose a methodology for semantic indexing and retrieval of images, based on techniques of image segmentation and classification combined with fuzzy reasoning. In the proposed knowledge-assisted analysis architecture a segmentation algorithm firstly generates a set of over-segmented regions. After that a region classification process is employed to assign semantic labels using a confidence degree and simultaneously merge regions based on their semantic similarity. This information comprises the assertional component of a fuzzy knowledge base which is used for the refinement of mistakenly classified regions and also for the extraction of rich implicit knowledge used for global image classification. This semantic metadata of images is stored in a knowledge repository by the fuzzy reasoning engine, also permitting image retrieval and ranking.

关键词： image classification

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Affine invariant curve matching using normalization and curvature scale-space

Affine invariant curve matching using normalization and curv...

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2008 International Workshop on Content-Based Multimedia Indexing, CBMI 2008

作者： Giannekou, V. Tzouveli, P. Avrithis, Y. Kollias, S. Image Video and Multimedia Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Iroon Polytexneiou 9 15773 Zografou Greece

ISBN: (纸本)9781424420445

In this paper, an affine invariant curve matching method using curvature scale-space and normalization is proposed. Prior to curve matching, curve normalization with respect to affine transformations is applied, allowing a lossless affine invariant curve representation. The maxima points of the curvature scale-space (CSS) image are then used to represent the normalized curve, while retaining the local properties of the curve. The matching algorithm that follows, matches the maxima sets of CSS images and the resulting matching cost provides a measure of similarity. The method's performance and robustness is evaluated through a variety of curves and affine transformations, obtaining precise shape similarity and retrieval. ©2008 IEEE.

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video coding using motion classification

Video coding using motion classification

引用

IEEE International Conference on image processing

作者： Marc Bosch Fengqing Zhu Edward J. Delp Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper we present a video coding approach similar to texture- based methods but based on motion models. We consider motion perception properties instead of spatial texture properties of the video sequence. We integrate a motion classification algorithm to separate foreground objects containing noticeable motion from the background. These background areas are labeled as skipped areas that are not encoded. After decoding, frame reconstruction is performed by inserting the skipped background into the decoded frames. We are able to show as much as 15% an improvement over previous texture- based implementations in terms of video compression efficiency.

关键词： video coding video sequences Decoding image coding image reconstruction Motion analysis Humans Pixel Cameras Layout

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