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  • Applying Soft Computing in Defining Spatial Relations
  • Applying Soft Computing in Defining Spatial Relations
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Applying Soft Computing in Defining Spatial Relations

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Applying Soft Computing in Defining Spatial Relations

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Beschreibung

Details

Einband

Gebundene Ausgabe

Erscheinungsdatum

04.09.2002

Herausgeber

Pascal Matsakis + weitere

Verlag

Physica

Seitenzahl

205

Beschreibung

Details

Einband

Gebundene Ausgabe

Erscheinungsdatum

04.09.2002

Herausgeber

Verlag

Physica

Seitenzahl

205

Maße (L/B/H)

24.1/16/1.7 cm

Gewicht

506 g

Auflage

2002

Sprache

Englisch

ISBN

978-3-7908-1504-7

Weitere Bände von Studies in Fuzziness and Soft Computing

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  • Applying Soft Computing in Defining Spatial Relations
  • Applying Soft Computing in Defining Spatial Relations
  • Fuzzifying Spatial Relations.- 1 Motivation.- 2 Imprecision in Spatial Relations.- 2.1 Conceptual Neighborhoods.- 2.2 Fuzzification of Allen Relations.- 3 Applying Allen’s Algorithm to Fuzzy Relations.- 4 Other Fuzzy Relations.- 5 Fuzzy Constraint Satisfaction.- 6 Conclusion.- Acknowledgement.- References.- Path Composition of Positional Relations Integrating Qualitative and Fuzzy Knowledge.- 1 Introduction.- 2 Composition of Positional Relations.- 2.1 Qualitative Distance Relations.- 2.2 Composition of Positional Relations.- 3 Path Composition.- 4 Integrating Qualitative and Fuzzy Knowledge.- 5 Fuzzy Knowledge Coming from Particular Distance Systems.- 6 Conclusions.- Acknowledgments.- References.- Spatial Relations Based on Dominance of Fuzzy Sets.- 1 Spatial Relations.- 1.1 Introduction.- 1.2 Modeling of Spatial Relations.- 1.3 Comparison of Definitions of Spatial Relations.- 1.4 Summary.- 2 Spatial Relations Among Fuzzy Subsets.- 2.1 Introduction.- 2.2 The Idea of Projections.- 2.3 Definitions of Spatial Relations for Fuzzy Objects.- 2.4 Properties of Spatial Relations for Fuzzy Objects.- 2.5 Separation Measure.- 2.6 The Model for Spatial Relationships.- 2.7 Results of Sample Systems.- 2.8 Conclusions.- References.- Mathematical Morphology and Spatial Relationships: Quantitative, Semi-Quantitative and Symbolic Settings.- 1 Introduction.- 2 Basic Morphological Operations, Fuzzy and Logical Extensions.- 2.1 Classical Morphology on Sets and Functions.- 2.2 Fuzzy Mathematical Morphology.- 2.3 Morpho-Logics.- 3 Computing Spatial Relationships from Mathematical Morphology: Quantitative and Semi-Quantitative Setting.- 3.1 Set Relationships.- 3.2 Adjacency.- 3.3 Distances.- 3.4 Directional Relative Position from Conditional Fuzzy Dilation.- 3.5 Example.- 4 Spatial Representations of Spatial Relationships.- 4.1 Spatial Fuzzy Sets as a Representation Framework.- 4.2 Set Relationships.- 4.3 Adjacency.- 4.4 Distances.- 4.5 Relative Directional Position.- 4.6 Example on Brain Structures.- 5 Symbolic Representations of Spatial Relationships.- 5.1 Topological Relationships.- 5.2 Distances.- 5.3 Directional Relative Position.- 6 Conclusion.- References.- Understanding the Spatial Organization of Image Regions by Means of Force Histograms: A Guided Tour.- 1 Introduction.- 2 The Notion of the Histogram of Forces.- 2.1 Description.- 2.2 Properties.- 2.3 Inverse Problem.- 3 Comparing Force Histograms.- 3.1 Principle.- 3.2 Application to Fuzzy Scene Matching.- 4 Defining Fuzzy Spatial Relations.- 4.1 Directional Relations.- 4.2 Other Spatial Relations.- 5 Generating Linguistic Spatial Descriptions.- 5.1 Principle.- 5.2 Application to Image Scene Description.- 5.3 Application to Human-Robot Communication.- 6 Conclusion.- Acknowledgments.- References.- Fuzzy Spatial Relationships and Mobile Agent Technology in Geospatial Information Systems.- 1 Introduction.- 2 Background.- 3 Fuzzy Directional Relationships and Querying.- 4 Extensions to the Model.- 4.1 Extensions to the Standard MBR Representation.- 4.2 Geometric Modeling Capabilities.- 4.3 An Extension for Expert System Implementation.- 4.4 A CLIPS Implementation.- 4.5 Fuzzy Querying of Binary Spatial Relationships.- 4.6 Modifications for Anomalous Cases.- 4.7 Oracle Implementation.- 5 Intelligent Agent Technology.- 5.1 Overview.- 5.2 Rule-Based Reasoning.- 5.3 Knowledge-Based Reasoning.- 5.4 Implementation.- 6 Summary and Future Work.- Acknowledgments.- References.- Using Fuzzy Spatial Relations to Control Movement Behavior of Mobile Objects in Spatially Explicit Ecological Models.- 1 Introduction.- 1.1 Information-Based Approaches to Ecological Modeling.- 1.2 Framework for Spatially Explicit Ecological Modeling.- 2 Modeling Habitat Landscape.- 2.1 Fuzzy Spatial Relations in Habitat Evaluation.- 2.2 An Example of Fuzzy Habitat Evaluation.- 2.3 Land Cover Classification and Habitat Modeling.- 3 Fuzzy Control of Spatial Movement.- 3.1 Perceptual Range as Fuzzy Spatial Relation.- 3.2 Controlling Foraging Movement.- 3.3 Controlling Exploratory Movement.- 3.4 Spatially Explicit Conspecific Interactions.- 4 Discussion.- 4.1 Fuzzy Rule-Base Models.- 4.2 Movement Direction and Memory.- 4.3 Fuzzy Logic and Robotics.- 4.4 Defining Fuzzy Spatial Relations.- 4.5 GIS Database Issues.- 4.6 Concluding Comment.- References.- A Fuzzy Set Model of Approximate Linguistic Terms in Descriptions of Binary Topological Relations Between Simple Regions.- 1 Introduction.- 2 Related Literature.- 2.1 The 9-Intersection Model of Topological Relations.- 2.2 Cognitive Aspects of Spatial Relations.- 2.3 Models of Spatial Relations Between Fuzzy Regions.- 2.4 Approximate Linguistic Terms in Descriptions of Spatial Relations.- 3 Fuzziness of Approximate Linguistic Terms — Preliminary Cognitive Evidences.- 3.1 Experimental Design.- 3.2 Results from Experiment One.- 3.3 Results from Experiment Two.- 4 A Fuzzy Set Model of Approximate Linguistic Terms.- 5 Discussion.- 6 Concluding Remarks.- Acknowledgements.- References.- About the Editors.