Abstract
Process of recognizing objects in binary images consists of image segmentation and pattern matching. If binary objects in the image are assumed to be separated, global features such as area, length of perimeter, or the ratio of the two can be used to recognize the objects in the image. However, if such an assumption is not valid, the global features can not be used but local features such as points or line ∙제1저자 : 문영인 ∙교신저자 : 구자영 ∙투고일 : 2014. 8. 20, 심사일 : 2014. 9. 5, 게재확정일 : 2014. 9. 27. * 단국대학교 컴퓨터학과 졸업(Dept. of Computer Science and Engineering, Dankook University) ** 단국대학교 소프트웨어학과(Dept. of Software Science, Dankook University) 64 Journal of The Korea Society of Computer and Information October 2014 segments should be used to recognize the objects. In this paper points with large curvature along the perimeter are chosen to be the feature points, and pairs of points selected from them are used as local features. Similarity of two local features are defined using elastic deformation energy for making the lengths and angles between gradient vectors at the end points same. Neighbour support value is defined and used for robust recognition of partially occluded binary objects. An experiment on Kimia-25 data showed that the proposed algorithm runs 4.5 times faster than the maximum clique algorithm with same recognition rate. ▸
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