|Author||: Mike E. Davies,Christopher C. James,Samer A. Abdallah,Mark D. Plumbley|
|Publisher||: Springer Science & Business Media|
|Release Date||: 2007-08-28|
|ISBN 10||: 3540744932|
|Pages||: 847 pages|
This book constitutes the refereed proceedings of the 7th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2007, held in London, UK, in September 2007. It covers algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing.
Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications. Furthermore, the research results previously scattered in many journals and conferences worldwide are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms and applications of BSS. Dr. Ganesh R. Naik works at University of Technology, Sydney, Australia; Dr. Wenwu Wang works at University of Surrey, UK.
A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation.
Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world’s experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications
|Author||: Dajiang Zhu,Jingwen Yan,Heng Huang,Li Shen,Paul M. Thompson,Carl-Fredrik Westin,Xavier Pennec,Sarang Joshi,Mads Nielsen,Tom Fletcher,Stanley Durrleman,Stefan Sommer|
|Publisher||: Springer Nature|
|Release Date||: 2019-10-10|
|ISBN 10||: 3030332268|
|Pages||: 230 pages|
This book constitutes the refereed joint proceedings of the 4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 16 full papers presented at MBAI 2019 and the 7 full papers presented at MFCA 2019 were carefully reviewed and selected. The MBAI papers intend to move forward the state of the art in multimodal brain image analysis, in terms of analysis methodologies, algorithms, software systems, validation approaches, benchmark datasets, neuroscience, and clinical applications. The MFCA papers are devoted to statistical and geometrical methods for modeling the variability of biological shapes. The goal is to foster the interactions between the mathematical community around shapes and the MICCAI community around computational anatomy applications.
|Author||: Karâa, Wahiba Ben Abdessalem|
|Publisher||: IGI Global|
|Release Date||: 2015-11-03|
|ISBN 10||: 1466688122|
|Pages||: 335 pages|
Every second, users produce large amounts of image data from medical and satellite imaging systems. Image mining techniques that are capable of extracting useful information from image data are becoming increasingly useful, especially in medicine and the health sciences. Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes addresses major techniques regarding image processing as a tool for disease identification and diagnosis, as well as treatment recommendation. Highlighting current research intended to advance the medical field, this publication is essential for use by researchers, advanced-level students, academicians, medical professionals, and technology developers. An essential addition to the reference material available in the field of medicine, this timely publication covers a range of applied research on data mining, image processing, computational simulation, data visualization, and image retrieval.
|Author||: Tulay Adali,Christian Jutten,Joao Marcos Travassos Romano,Allan Kardec Barros|
|Publisher||: Springer Science & Business Media|
|Release Date||: 2009-02-25|
|ISBN 10||: 3642005985|
|Pages||: 785 pages|
This volume contains the papers presented at the 8th International Conf- ence on Independent Component Analysis (ICA) and Source Separation held in Paraty, Brazil, March 15–18, 2009. This year's event resulted from scienti?c collaborations between a team of researchers from ?ve di?erent Brazilian u- versities and received the support of the Brazilian Telecommunications Society (SBrT) as well as the ?nancial sponsorship of CNPq, CAPES and FAPERJ. Independent component analysis and signal separation is one of the most - citing current areas of research in statistical signal processing and unsupervised machine learning. The area has received attention from severalresearchcom- nities including machine learning, neural networks, statistical signal processing and Bayesian modeling. Independent component analysis and signal separation has applications at the intersection of many science and engineering disciplines concerned with understanding and extracting useful information from data as diverse as neuronal activity and brain images, bioinformatics, communications, the World Wide Web, audio, video, sensor signals, and time series.
A systematic exploration of both classic and contemporaryalgorithms in blind source separation with practical casestudies The book presents an overview of Blind Source Separation, arelatively new signal processing method. Due to themultidisciplinary nature of the subject, the book has been writtenso as to appeal to an audience from very different backgrounds.Basic mathematical skills (e.g. on matrix algebra and foundationsof probability theory) are essential in order to understand thealgorithms, although the book is written in an introductory,accessible style. This book offers a general overview of the basics of BlindSource Separation, important solutions and algorithms, and in-depthcoverage of applications in image feature extraction, remotesensing image fusion, mixed-pixel decomposition of SAR images,image object recognition fMRI medical image processing, geochemicaland geophysical data mining, mineral resources prediction andgeoanomalies information recognition. Firstly, the background andtheory basics of blind source separation are introduced, whichprovides the foundation for the following work. Matrix operation,foundations of probability theory and information theory basics areincluded here. There follows the fundamental mathematical model andfairly new but relatively established blind source separationalgorithms, such as Independent Component Analysis (ICA) and itsimproved algorithms (Fast ICA, Maximum Likelihood ICA, OvercompleteICA, Kernel ICA, Flexible ICA, Non-negative ICA, Constrained ICA,Optimised ICA). The last part of the book considers the very recentalgorithms in BSS e.g. Sparse Component Analysis (SCA) andNon-negative Matrix Factorization (NMF). Meanwhile, in-depth casesare presented for each algorithm in order to help the readerunderstand the algorithm and its application field. A systematic exploration of both classic and contemporaryalgorithms in blind source separation with practical casestudies Presents new improved algorithms aimed at differentapplications, such as image feature extraction, remote sensingimage fusion, mixed-pixel decomposition of SAR images, image objectrecognition, and MRI medical image processing With applications in geochemical and geophysical data mining,mineral resources prediction and geoanomalies informationrecognition Written by an expert team with accredited innovations in blindsource separation and its applications in natural science Accompanying website includes a software system providing codesfor most of the algorithms mentioned in the book, enhancing thelearning experience Essential reading for postgraduate students and researchersengaged in the area of signal processing, data mining, imageprocessing and recognition, information, geosciences, lifesciences.
|Release Date||: 2004|
|Pages||: 329 pages|
With solid theoretical foundations and numerous potential applications, Blind Signal Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume unifies and extends the theories of adaptive blind signal and image processing and provides practical and efficient algorithms for blind source separation: Independent, Principal, Minor Component Analysis, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind Signal and Image Processing delivers an unprecedented collection of useful techniques for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable signals and data. Offers a broad coverage of blind signal processing techniques and algorithms both from a theoretical and practical point of view Presents more than 50 simple algorithms that can be easily modified to suit the reader's specific real world problems Provides a guide to fundamental mathematics of multi-input, multi-output and multi-sensory systems Includes illustrative worked examples, computer simulations, tables, detailed graphs and conceptual models within self contained chapters to assist self study Accompanying CD-ROM features an electronic, interactive version of the book with fully coloured figures and text. C and MATLAB user-friendly software packages are also provided MATLAB is a registered trademark of The MathWorks, Inc. By providing a detailed introduction to BSP, as well as presenting new results and recent developments, this informative and inspiring work will appeal to researchers, postgraduate students, engineers and scientists working in biomedical engineering, communications, electronics, computer science, optimisations, finance, geophysics and neural networks.
|Author||: Leszek Rutkowski,Jörg Siekmann,Ryszard Tadeusiewicz,Lotfi A. Zadeh|
|Release Date||: 2004-05-18|
|ISBN 10||: 3540248447|
|Pages||: 1210 pages|
This book constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004, held in Zakopane, Poland in June 2004. The 172 revised contributed papers presented together with 17 invited papers were carefully reviewed and selected from 250 submissions. The papers are organized in topical sections on neural networks, fuzzy systems, evolutionary algorithms, rough sets, soft computing in classification, image processing, robotics, multiagent systems, problems in AI, intelligent control, modeling and system identification, medical applications, mechanical applications, and applications in various fields.
This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book gives an overview on blind source separation where three promising blind separation techniques that can tackle mutually correlated sources are presented. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.