Machine Learning And Adaptive Morphological Operators
نویسندگان
چکیده
This work proposes the use of machine learning methods applied to the construction of a gray level adaptive hitor-miss morphological operator. Because they are adaptive and translation invariant, it is expected that these operators can be better utilized for the process of pattern recognition. In a first approach, the investigated adaptive model is inspired on the Vector Quantization Unsupervised Learn Rule and developed through Elementary Look-Up Tables (ELUTs) formalism of elementary morphological operators in gray level images. Keywords—mathematical morphology; pattern recognition; adaptive operator
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