Overview
- Editors:
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Petra Perner
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Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany
- First edited book that deals with the special topic of signals and images within Case-Based Reasoning (CBR)
- Includes supplementary material: sn.pub/extras
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Table of contents (14 chapters)
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- A. Bagherjeiran, C. F. Eick
Pages 91-126
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- L. Bobrowski, M. Topczewska
Pages 127-148
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- J. M. Corchado, J. Aiken, J. Bajo
Pages 213-246
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- R. Schmidt, T. Waligora, O. Vorobieva
Pages 285-317
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- M. Frucci, P. Perner, G. Sanniti di Baja
Pages 319-353
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- L. G. Shapiro, I. Atmosukarto, H. Cho, H. J. Lin, S. Ruiz-Correa, J. Yuen
Pages 355-387
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- D. C. Wilson, D. O’Sullivan
Pages 389-418
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About this book
This book is the ?rst edited book that deals with the special topic of signals and images within case-based reasoning (CBR). Signal-interpreting systems are becoming increasingly popular in medical, industrial, ecological, biotechnological and many other applications. Existing statisticalandknowledge-basedtechniqueslackrobustness,accuracy,and?- ibility. New strategies are needed that can adapt to changing environmental conditions, signal variation, user needs and process requirements. Introducing CBRstrategiesintosignal-interpretingsystemscansatisfytheserequirements. CBR can be used to control the signal-processing process in all phases of a signal-interpreting system to derive information of the highest possible qu- ity. Beyond this CBR o?ers di?erent learning capabilities, for all phases of a signal-interpretingsystem,thatsatisfydi?erentneedsduringthedevelopment process of a signal-interpreting system. In the outline of this book we summarize under the term “signal” signals of 1-dimensional, 2-dimensional or 3-dimensional nature. The unique data and the necessary computation techniques require ext- ordinary case representations, similarity measures and CBR strategies to be utilised. Signalinterpretation(1D,2D,or3Dsignalinterpretation)istheprocessof mapping the numerical representation of a signal into logical representations suitable for signal descriptions. A signal-interpreting system must be able to extract symbolic features from the raw data e.g., the image (e.g., irregular structure inside the nodule, area of calci?cation, and sharp margin). This is a complex process; the signal passes through several general processing steps before the ?nal symbolic description is obtained. The structure of the book is divided into a theoretical part and intoan application-oriented part.
Editors and Affiliations
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Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany
Petra Perner
