Optical Acquisition, Image and Data Compression.
Abstract
In this report a new shape classification scheme is proposed which uses the image processing formalism of associative memory mapping. This scalar transform technique is applied to two-dimensional (2D) images. The shape description is the centrodial profile which is the radius as a function of arc length parametrization of the boundary. Other one-dimensional (ID) representations are also discussed. The scheme is applicable to both full-and partial-view recognition problems. The restoration of degraded images, either due to occulsion or other forms of information loss, is optimal in the least squares sense. Two different approaches to decision aids are explored. In one, forced binary decisions are made. In the other, a set of a posteriori probabilities is calculated. In both cases fast inferences can be calculated by optical matrix operations. There are many cases, both in signal and in image processing applications, where the eigenvalues and vectors of particular matrix operators are required. Furthermore, in many situations, on a particular matrix operator, there is a priori information available. Most eigenvalue algorithms do not utilize all the available information. In this paper, the use of the Lie algebra of matrix operators is suggested for systolic array eigenvector and value computation. After a brief survey of the revelant Lie algebra for such computation, a number of possible Lie signal processing algebra examples are presented.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 1986
- Accession Number
- ADA178508
Entities
People
- George Eichmann
Organizations
- City College of New York