Automatic programming of binary morphological machines by design of statistically optimal operators in the context of computational learning theory
Academic Article
Overview
Identity
Additional Document Info
Other
View All
Overview
abstract
Representation of set operators by artificial neural networks and design of such operators by inference of network parameters is a popular technique in binary image analysis. We propose an alternative to this technique: automatic programming of morphological machines (MMachs) by the design of statistically optimal operators. We propose a formulation of the procedure for designing set operators that extends the one stated by Dougherty for binary image restoration, show the relation of this new formulation with the one stated by Haussler for learning Boolean concepts in the context of machine learning theory (which usually is applied to neural networks), present a new learning algorithm for Boolean concepts represented as MMach programs, and give some application examples in binary image analysis. 1997 SPIE and IS&T.
