Data Fusion for Sensory Information Processing Systems

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

    PDF

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.

• Frames
• Stereo
• algorithms
• classification
• cognition
• sensor
• shading

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

  James J. Clark, Alan L. Yuille

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