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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 International Journal of Control Automation and Systems International Journal of Control Automation and System Vol.4 No.5
- 발행연도
- 2006.10
- 수록면
- 637 - 644 (8page)
이용수
초록· 키워드
오류제보하기
Biomedical image is often complex. An applied image analysis system should deal with the images which are of quite low quality and are challenging to segment. This paper presents a framework for color cell image segmentation by learning and classification online. It is a robust two-stage scheme using kernel method and watershed transform. In first stage, a two-class SVM is employed to discriminate the pixels of object from background; where the SVM is trained on the data which has been analyzed using the mean shift procedure. A real-time training strategy is also developed for SVM. In second stage, as the post-processing, local watershed transform is used to separate clustering cells. Comparison with the SSF (Scale space filter) and classical watershed-based algorithm (those are often employed for cell image segmentation) is given. Experimental results demonstrate that the new method is more accurate and robust than compared methods.
목차
Abstract
1. INTRODUCTION
2. PIXEL CLASSIFICATION BY SYM
3. LOCAL WATERSHED TRANSFORM FOR DECLUSTERING
4. EXPERIMENTAL RESULTS
5. CONCLUSIONS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2010-560-001677614