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Infected Part Detection based on Affinity Propagation Clustering
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- 발행기관
- 보안공학연구지원센터(IJSEIA) 바로가기
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- 간행물
- International Journal of Software Engineering and Its Applications SCOPUS 바로가기
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- 통권
- Vol.10 No.7 (2016.07)바로가기
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- 페이지
- pp.39-52
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A281770
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Manual identification of defected objects consumes time and effort. These leading researchers try to find out an automatic infected objects detection systems to reduce these denigrate issues, which affects trade business fields, as an example, infected fruits or vegetables in agricultural field. This paper presents an image segmentation method based on affinity propagation (AP) clustering algorithm for detecting infected part in fruits or vegetables. Results show that this methodology is good comparing to K-means algorithm, which gives good results. Nevertheless, AP outperforms that does not need pre-specify cluster number, which is needed in K-means. Some deficiencies occur when using traditional AP, but using sparse version of AP overcomes most of these deficiencies. Extra feature of AP that it works better than K-means as cluster number is increasing or complexity of infected objects is amplifying. AP works better than K-means in widespread and various sized defected regions. Another contribution in this paper, choosing adequate color space provides preferable results. Experimental results clarify that NTSC or YCbCr color space are more stable to act as image color space since they enhance Silhouette values rhythmically. However, methodology presented in this paper needs to collaborate with other image techniques, as indexed color techniques and lossless compression methods, to overcome operation speed problem. In addition, more enhancements are anticipated when using adaptive AP, as it introduces solutions through adaptive damping, adaptive preference scanning and adaptive escaping oscillations.
목차
Abstract
1. Introduction
2. Related Work
3. AP Clustering Algorithm
4. Proposed Methodology
5. Experimental Results
6. Conclusion
References
키워드
AP clustering Color Space Defect Fruit Defect Vegetables Image Processing k-means Segmentation저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJSEIA) [Science & Engineering Research Support Center, Republic of Korea(IJSEIA)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Software Engineering and Its Applications
- 간기
- 월간
- pISSN
- 1738-9984
- 수록기간
- 2008~2016
- 등재여부
- SCOPUS
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Software Engineering and Its Applications Vol.10 No.7
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