A probabilistic approach to the alopex process using moment invariants of images

Author

Chon ; Micheli-Tzanakou, E.

Author_Institution

Dept. of Biomed. Eng., Rutgers Univ., Piscataway, NJ, USA

fYear

1989

fDate

0-0 1989

Abstract

Summary form only given, as follows. An iterative optimization technique has been developed that uses feedback in order to maximize the response of a system. The cost function for this process is problem dependent and therefore quite flexible. The method has been applied successfully to different optimization problems such as pattern recognition, reception field studies in the visual system of animals, curve fitting, etc. The authors explore the possibility of using probabilities and moment invariants in speeding up the convergence of the process.<>

Keywords

convergence of numerical methods; iterative methods; neural nets; optimisation; pattern recognition; picture processing; visual perception; alopex process; convergence; curve fitting; feedback; iterative optimization technique; moment invariants of images; optimization problems; pattern recognition; probabilistic approach; reception field studies; visual system of animals; Convergence of numerical methods; Image processing; Iterative methods; Neural networks; Optimization methods; Pattern recognition; Visual system;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1989. IJCNN., International Joint Conference on

Conference_Location

Washington, DC, USA

Type

conf

DOI

10.1109/IJCNN.1989.118440

Filename

118440