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
- 학술저널
- 저자정보
- 발행연도
- 2021.8
- 수록면
- 502 - 509 (8page)
- DOI
- 10.5302/J.ICROS.2021.21.0045
이용수
초록· 키워드
오류제보하기
This work proposes a scale-invariant instance segmentation method for images acquired from a real-time camera. It is challenging to detect and segment an exact shape by removing background (named as an instance) of a deformable semi-solid object such as food materials. In this work, we consider the segmentation with the cases of various sizes of an object and multiple objects overlapped with each. To do this, we address an augmented dataset generation method, which extends dataset from small number of base objects, a fundamental dataset. Our method is based upon data augmentation, which is well known that it is an effective way to improve the segmentation performance. Our method addresses the generation of dataset with various scales using small number of original dataset. It is relatively simple in method but provides better performance. We also proposed how to choose a target object(food material) with its centroid for grasping. Through diverse experiments using real-time images, we demonstrate that the proposed algorithm segments scale-invariant object masks and is successfully implemented for a robotic hand to grasp a food material. It is also compared with the state-of-the-art segmentation algorithm. As a result, the proposed method shows 74%, 85%, and 78% in accuracy, recall, and precision while the original dataset shows 67%, 79%, and 70%, respectively.
목차
Abstract
I. Introduction
II. Related Work
III. Problem Statement
IV. Dataset Generation Method
V. Food Materials Segmentation
VI. Experimental Results
VII. Conclusion
REFERENCES
참고문헌 (28)
참고문헌 신청
M. Javaid, A. Haleem, A. Vaish, R. Vaishya, and K. P. Iyengar, “Robotics applications in covid-19: A review,” Journal of Industrial Integration and Management, vol. 5, no. 4, pp. 441-451, Dec. 2020.
I. Levner and H. Zhang, “Classification-driven watershed segmentation,” IEEE Transactions on Image Processing, vol. 16, no. 5, pp. 1437-1445, May 2007.
Z. Lin and L. S. Davis, “Shape-based human detection and segmentation via hierarchical part-template matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 604-618, Apr. 2010.
R. Brunelli, Template matching techniques in computer vision: Theory and practice, Wiley, 2009.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski “ORB: An efficient alternative to SIFT or SURF,” International Conference on Computer Vision, pp. 2564-2571, 2011.
I. Levner and H. Zhang, “Classification-driven watershed segmentation,” IEEE Transactions on Image Processing, vol. 16, no. 5, pp. 1437-1445, May 2007.
Z. Lin and L. S. Davis, “Shape-based human detection and segmentation via hierarchical part-template matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 604-618, Apr. 2010.
R. Brunelli, Template matching techniques in computer vision: Theory and practice, Wiley, 2009.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski “ORB: An efficient alternative to SIFT or SURF,” International Conference on Computer Vision, pp. 2564-2571, 2011.
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