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http://dx.doi.org/10.3795/KSME-A.2004.28.2.125

An Image Segmentation Method and Similarity Measurement Using fuzzy Algorithm for Object Recognition  

Kim, Dong-Gi (다이모스(주))
Lee, Seong-Gyu (충남대학교 대학원 기계설계공학과)
Lee, Moon-Wook (특허청)
Kang, E-Sok (충남대학교 기계설계공학과)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.28, no.2, 2004 , pp. 125-132 More about this Journal
Abstract
In this paper, we propose a new two-stage segmentation method for the effective object recognition which uses region-growing algorithm and k-means clustering method. At first, an image is segmented into many small regions via region growing algorithm. And then the segmented small regions are merged in several regions so that the regions of an object may be included in the same region using typical k-means clustering method. This paper also establishes similarity measurement which is useful for object recognition in an image. Similarity is measured by fuzzy system whose input variables are compactness, magnitude of biasness and orientation of biasness of the object image, which are geometrical features of the object. To verify the effectiveness of the proposed two-stage segmentation method and similarity measurement, experiments for object recognition were made and the results show that they are applicable to object recognition under normal circumstance as well as under abnormal circumstance of being.
Keywords
Image Segmentation; Similarity; Region-Growing; Compactness;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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