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.

Klappentext

This book is the first 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 statistical and knowledge-based techniques lack robustness, accuracy and flexibility. New strategies are needed that can adapt to changing environmental conditions, signal variation, user needs and process requirements. Introducing CBR strategies into signal-interpreting systems can satisfy these requirements. 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 quality. Beyond this CBR offers different learning capabilities, for all phases of a signal-interpreting system, that satisfy different needs during the development process of a signal-interpreting system.

The structure of the book is divided into a theoretical part and into an application-oriented part. Scientists and computer science experts from industry, medicine and biotechnology who like to work on the special topics of CBR for signals and images will find this work useful. Although case-based reasoning is often not a standard lecture at universities we hope we to also inspire PhD students to deal with this topic.

Inhalt
to Case-Based Reasoning for Signals and Images.- Similarity.- Distance Function Learning for Supervised Similarity Assessment.- Induction of Similarity Measures for Case Based Reasoning Through Separable Data Transformations.- Graph Matching.- Memory Structures and Organization in Case-Based Reasoning.- Learning a Statistical Model for Performance Prediction in Case-Based Reasoning.- A CBR Agent for Monitoring the Carbon Dioxide Exchange Rate from Satellite Images.- Extracting Knowledge from Sensor Signals for Case-Based Reasoning with Longitudinal Time Series Data.- Prototypes and Case-Based Reasoning for Medical Applications.- Case-Based Reasoning for Image Segmentation by Watershed Transformation.- Similarity-Based Retrieval for Biomedical Applications.- Medical Imagery in Case-Based Reasoning.- Instance-Based Relevance Feedback in Image Retrieval Using Dissimilarity Spaces.