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
- 2014.10
- 수록면
- 88 - 93 (6page)
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초록· 키워드
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This paper presents a segmentation method of multiple object regions based on visual saliency. Our method comprises three steps. First, attentional points are detected using saliency maps (SMs). Subsequently, regions of interest (RoIs) are extracted using scale-invariant feature transform (SIFT). Finally, foreground regions are extracted as object regions using GrabCut. Using RoIs as teaching signals, our method achieved automatic segmentation of multiple objects without learning in advance. As experimentally obtained results obtained using PASCAL2011 dataset, attentional points were extracted correctly from 18 images for two objects and from 25 images for single objects. We obtained segmentation accuracies: 64.1%, precision; 62.1%, recall, and 57.4%, F-measure. Moreover, we applied our method to time-series images obtained using a mobile robot. Attentional points were extracted correctly for seven images for two objects and three images for single objects from ten images. We obtained segmentation accuracies of 58.0%, precision; 63.1%, recall, and 58.1%, F-measure.
목차
Abstract
1. INTRODUCTION
2. PROPOSED METHOD
3. BASIC EXPERIMENT USING AN OPEN DATASET
4. APPLICATION TO ROBOT VISION
5. DISCUSSION
6. CONCLUSION
REFERENCES
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