지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.10
- 수록면
- 944 - 947 (4page)
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초록· 키워드
오류제보하기
Vision is becoming more and more common in applications such as localization, autonomous navigation, path finding and many other computer vision applications. This paper presents an improved technique for feature matching in the stereo images captured by the autonomous vehicle. The Scale Invariant Feature Transform (SIFT) algorithm is used to extract distinctive invariant features from images but this algorithm has a high complexity and a long computational time. In order to reduce the computation time, this paper proposes a SIFT improvement technique based on a Self-Organizing Map (SOM) to perform the matching procedure more efficiently for feature matching problems. Experimental results on real stereo images show that the proposed algorithm performs feature group matching with lower computation time than the original SIFT algorithm. The results showing improvement over the original SIFT are validated through matching examples between different pairs of stereo images. The proposed algorithm can be applied to stereo vision based autonomous vehicle navigation for obstacle avoidance, as well as many other feature matching and computer vision applications.
목차
Abstract
1. INTRODUCTION
2. SCALE INVARIANT FEATURETRANSFORM (SIFT)
3. SELF-ORGANIZING MAP BASED FEATURE MATCHING
4. EXPERIMENTAL RESULTS AND DISCUSSION
5. CONCLUSION AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2014-569-000913887