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Data Fusion for Sensory Information Processing Systems

  • Book
  • © 1990

Overview

Authors:
  1. James J. Clark

    1. Division of Applied Sciences, Harvard University, Cambridge, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Alan L. Yuille

    1. Division of Applied Sciences, Harvard University, Cambridge, USA

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 105)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xix
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  2. Introduction: The Role of Data Fusion in Sensory Systems

    • James J. Clark, Alan L. Yuille
    Pages 1-16

  3. Bayesian Sensory Information Processing

    • James J. Clark, Alan L. Yuille
    Pages 17-38

  4. Information Processing Using Energy Function Minimization

    • James J. Clark, Alan L. Yuille
    Pages 39-69

  5. Weakly vs. Strongly Coupled Data Fusion: A Classification of Fusional Methods

    • James J. Clark, Alan L. Yuille
    Pages 71-104

  6. Data Fusion Applied to Feature Based Stereo Algorithms

    • James J. Clark, Alan L. Yuille
    Pages 105-135

  7. Fusing Binocular and Monocular Depth Cues

    • James J. Clark, Alan L. Yuille
    Pages 137-146

  8. Data Fusion in Shape From Shading Algorithms

    • James J. Clark, Alan L. Yuille
    Pages 147-180

  9. Temporal Aspects of Data Fusion

    • James J. Clark, Alan L. Yuille
    Pages 181-215

  10. Towards a Constraint Based Theory of Sensory Data Fusion

    • James J. Clark, Alan L. Yuille
    Pages 217-222

  11. Back Matter

    Pages 223-242
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Keywords

About this book

The science associated with the development of artificial sen­ sory systems is occupied primarily with determining how information about the world can be extracted from sensory data. For example, computational vision is, for the most part, concerned with the de­ velopment of algorithms for distilling information about the world and recognition of various objects in the environ­ (e. g. localization ment) from visual images (e. g. photographs or video frames). There are often a multitude of ways in which a specific piece of informa­ tion about the world can be obtained from sensory data. A subarea of research into sensory systems has arisen which is concerned with methods for combining these various information sources. This field is known as data fusion, or sensor fusion. The literature on data fusion is extensive, indicating the intense interest in this topic, but is quite chaotic. There are no accepted approaches, save for a few special cases, and many of the best methods are ad hoc. This book represents our attempt at providing a mathematical foundation upon which data fusion algorithms can be constructed and analyzed. The methodology that we present in this text is mo­ tivated by a strong belief in the importance of constraints in sensory information processing systems. In our view, data fusion is best un­ derstood as the embedding of multiple constraints on the solution to a sensory information processing problem into the solution pro­ cess.

Authors and Affiliations

  • Division of Applied Sciences, Harvard University, Cambridge, USA

    James J. Clark, Alan L. Yuille

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