Fuzzy Aggregation with Artificial Color filters
Artificial Color filters are designed to attenuate some pixels and pass others. The pass/attenuate decision is made on the basis of the learned association of spectral components with user-defined concepts. In earlier work, it has been shown that there are various ways to design Artificial Color filters using multiple user-designated classes and those filters are subjected to useful manipulations such as image processing and Boolean Aggregation. The Artificial Color filtering has always been binary. Therefore, the Boolean logic was the only choice for aggregating filters. This paper shows how to fuzzify Artificial Color filters. Fuzzy logic subsumes Boolean logic and can do so in many ways. Several different fuzzy T-norms are applied to Artificial Color filters to illustrate the richness in aggregation. Margin Setting, a supervised statistical pattern recognition method to train the filters, is very conservative in what is definitely assigned to a class (?=1) ( ? = 1 ) while allowing a useful gradation of membership (??1) ( ? ? 1 ) for other cases. A parametric exploration of these effects for an image is presented.
artificial color margin setting image algebra image fuzzification image segmentation