Morphological methods in image and signal processing
Morphological methods in image and signal processing
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Vagueness in Spatial Data: Rough Set and Egg-Yolk Approaches
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Extensions to the fuzzy pointed set with applications to image processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper we consider the problem of detecting binary objects. We present a method for constructing a gray-scaled (or, fuzzy) template for use in correlation-based matching of Boolean images, using rough sets. First, we represent the binary images in the morphological sense - that is - as sets. Next, we assume a cause for spatial uncertainty that is quite common in machine vision applications and present a methodology for modeling it indirectly in the construction of the template. Then we show how rough sets can be used to determine the matching probabilities constructively, rather than through trial and error, as is usually the case. Our technique is computationally efficient and is superior to correlation-based techniques, which can be easily fooled and automates the hand-selection of structuring elements for the hit-or-miss transform technique, both of which are usually used to solve this problem.