유전자알고리즘을 이용한 영상분할 문턱값의 자동선정에 관한 연구

다국어 초록 (Multilingual Abstract)

In this paper, we focus on the issue of automatic selection for multi-level threshold, and we greatly improve the efficiency of Otsu’s method for image segmentation based on genetic algorithm. We have investigated and evaluated the performance of the Otsu and Valley-emphasis threshold methods. Based on this observation we propose a method for automatic threshold method that segments an image into more than two regions with high performance and processing in real-time. Our paper introduced new peak detection, combines with evolution algorithm using MAGA (Modified Adaptive Genetic Algorithm) and HCA (Hill Climbing Algorithm), to find the best threshold automatically, accurately, and quickly. The experimental results show that the proposed evolutionary algorithm achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.

목차 (Table of Contents)

• Abstract
• Ⅰ. 서론
• Ⅱ. 기존의 문턱값 자동선정 기법
• Ⅲ. 제안된 문턱값 자동선정 기법
• Ⅳ. 실험결과 및 고찰
• Ⅴ. 결론
• 참고문헌

참고문헌 (Reference)

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14 N. Otsu, "A threshold selection method from gray-level histograms" 9 (9): 62-66, 1979

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17 P. S. Liao, "A fast algorithm for multilevel thresholding" 17 : 713-727, 2001