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FUZZY LOGIC IN INTELLIGENT SYSTEMS AND COMPUTER VISION
IMAGE UNDERSTANDING AND ANALYSIS
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activity algorithm analysis applied approach assumed attributes camera cells classification complex components Computer considered coordinates corresponding decision defined depth derived described detection determine developed direction disparity distance domain dynamic edge effects equation error estimation example experiment extraction feature field Figure filter flow frames function fuzzy given hand horizontal human input knowledge layer log-polar machine matching measurement method motion neural node noise object obtained operator optical original output parameters pattern performed pixels position possible presented problem processing proposed range recognition region relationships relative representation represented respectively response rotation rule scale scene scheme sequence shape shown shows simulation solution space spatial step structure Table technique transform translation tree values vector vision visual