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Case-Based Reasoning on Images and Signals

Contributor(s): Perner, Petra [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 73Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: X, 436 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540731801.Subject(s): Engineering | Multimedia information systems | Artificial intelligence | Computer graphics | Applied mathematics | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Multimedia Information Systems | Artificial Intelligence (incl. Robotics) | Computer Imaging, Vision, Pattern Recognition and GraphicsDDC classification: 519 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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