In this paper, two methods of extracting objects are compared through application to underwater images: one method is to extract objects by removing the background and quantifying it into a codebook by measuring the Mahalanobis distance for accurate object segmentation and extraction, and the other is to extract objects by removing the background and quantifying it into a codebook by measuring the Euclidean distance. In an experiment relating to the comparison and analysis, a standard underwater sample image was learned. Then, the background color’s average value and the input image’s stochastic distances were computed through the color similarity algorithm, and then the object was extracted after the background color could be removed. For the performance evaluation on the two algorithms, an underwater image was used to run some computer simulations. The experiment showed that an image applied with the color similarity algorithm had a better image segmentation performance than that with the different image technique.
목차
Abstract 1. Introduction 2. Related Work 3. The Design of Algorithms for Comparison 3.1. Underwater Background Learning 3.2. Similarity Measure-Mahalanobis Distances 3.3. Similarity Measure-Euclidean Distances 4. Experiments 4.1. Experimental Methods 4.2. Results 5. Conclusion References
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
간행물명
International Journal of Software Engineering and Its Applications
간기
월간
pISSN
1738-9984
수록기간
2008~2016
등재여부
SCOPUS
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Software Engineering and Its Applications Vol.7 No.6