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.
Product Identifiers
Publisher
Springer
ISBN-13
9780792391203
eBay Product ID (ePID)
95242443
Product Key Features
Book Title
Data Fusion for Sensory Information Processing Systems
Author
James J. Clark, Alan L. Yuille
Format
Hardcover
Language
English
Topic
Computer Science
Publication Year
1990
Type
Textbook
Number of Pages
244 Pages
Dimensions
Item Height
234mm
Item Width
156mm
Volume
105
Item Weight
1220g
Additional Product Features
Title_Author
Alan L. Yuille, James J. Clark
Topic Area
Material Science, Mechanical Engineering
Series Title
The Springer International Series in Engineering and Computer Science