Theory of T-norms and fuzzy inference methods
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Surface Identification Using the Dichromatic Reflection Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Studies in fuzzy relations using triangular norms
Information Sciences: an International Journal
Color image segmentation using fuzzy C-means and eigenspace projections
Signal Processing
On the use of Hamacher's t-norms family for information aggregation
Information Sciences: an International Journal
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Information Sciences—Informatics and Computer Science: An International Journal
Joint exploration of artificial color and margin setting: an innovative approach in color image segmentation
Use of Artificial Color filtering to improve iris recognition and searching
Pattern Recognition Letters
Designing spectral sensitivity curves for use with Artificial Color
Pattern Recognition
A rough margin based support vector machine
Information Sciences: an International Journal
Fuzzy filters and fuzzy prime filters of bounded Rl-monoids and pseudo BL-algebras
Information Sciences: an International Journal
Using a minimal fuzzy covering in decision-making problems
Information Sciences: an International Journal
Artificial and biological color band design as spectral compression
Image and Vision Computing
Segmentation of color images using multiscale clustering and graph theoretic region synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy shell clustering algorithms in image processing: fuzzy C-rectangular and 2-rectangular shells
IEEE Transactions on Fuzzy Systems
Aggregation functions: Construction methods, conjunctive, disjunctive and mixed classes
Information Sciences: an International Journal
Class-dependent rough-fuzzy granular space, dispersion index and classification
Pattern Recognition
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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 (@m=1) while allowing a useful gradation of membership (@m=