An object recognition method using the improved snake algorithm

Authors:
Qian Zhang;Sung-Jong Eun;Hyeonjin Kim;Taeg-Keun Whangbo
Affiliations:
Taishan University, Taian City, Shandong Province, China;Kyungwon University, Sujung-Gu, Seongnam, Gyunggi-Do, Korea;Seoul National University, Jongno-Gu, Seoul, Korea;Kyungwon University, Sujung-Gu, Seongnam, Gyunggi-Do, Korea
Venue:
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Year:
2012

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Abstract

The general object recognition method is based on the area segmentation algorithm. Among the many area segmentation methods, the representative Active Contour Model (ACM), the snake model, was used in this paper for effective object recognition. The proposed method involved snake point allotment, contour line convergence, and improvement of the corrected portions, and the method recognized objects stably as a result of medical imaging. This study was conducted to minimize the post-processing cost of area segmentation. Future studies will be conducted to develop an algorithm for more efficient and accurate object recognition by complementing corrective work with contour line convergence work.