Application of Fuzzy Theory to Image Recognition of Agricultural Products.

Abstract

This paper describes a new technology on image recognition of objects using fuzzy theory. The standard method of pattern recognition is geometrical pattern matching. This is accomplished by comparing an object with a standard pattern, which is effective in cases having little ambiguity, such as industrial products. However, it is generally difficult to recognize natural products, because these objects have essential fuzziness caused by the differences is shapes, sizes and colors. In this paper, pictorial pattern recognition including these kinds of fuzziness is investigated, and a new image recognition system is proposed with the application of fuzzy theory. Through discrimination experiments made for ten categories such as apples, bannas, lemonns and others, satisfactory results are obtained. At result, it is certified that the pattern recognition proposed here is more effective to recognition of resemble objects categories.

Abstract from the paper’s OpenAlex record, shown for indexing and research. Copyright remains with the authors or the publisher. Read at the source ↗

The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.