This study presents a tri-level thresholding method for image segmentation with invasive weed optimization (IWO) algorithm. The objective of the proposed approach is to handle the nonextensivity and vagueness of image in segmentation, in the meanwhile to reduce the computation time. In this study, the histogram of image is converted to fuzzy domain by membership function firstly. Then the thresholding method is constructed through maximizing the sum of nonextensive entropy of subsets of the each part of fuzzy histogram. The IWO algorithm is used to search the optimal thresholds to reduce the computation time in the new method. Experiments on synthetic and real-world images are given to demonstrate the effectiveness of the proposed approach compared with the other methods.
목차
Abstract 1. Introduction 2. Thresholding by Fuzzy Set and Nonextensive Entropy 2.1. Convert the Image Histogram to Fuzzy Domain 2.2. Thresholding through Nonextensive Fuzzy Entropy 3. Thresholds Selection using Invasive Weed Optimization 4. Experimental Results and Analysis 4.1. Performance Evaluation 4.2. Experiments on Real Images 5. Conclusion Acknowledgements References
보안공학연구지원센터(IJSIP) [Science & Engineering Research Support Center, Republic of Korea(IJSIP)]
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
간행물명
International Journal of Signal Processing, Image Processing and Pattern Recognition
간기
격월간
pISSN
2005-4254
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.7 No.6