Abstract
We present a multi-instance object segmentation algorithm to tackle occlusions. As an object is split into two parts by an occluder, it is nearly impossible to group the two separate regions into an instance by purely bottomup schemes. To address this problem, we propose to incorporate top-down category specific reasoning and shape prediction through exemplars into an intuitive energy minimization framework. We perform extensive evaluations of our method on the challenging PASCAL VOC 2012 segmentation set. The proposed algorithm achieves favorable results on the joint detection and segmentation task against the state-of-the-art method both quantitatively and qualitatively.
| Original language | English |
|---|---|
| Title of host publication | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 |
| Publisher | IEEE Computer Society |
| Pages | 3470-3478 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781467369640 |
| DOIs | |
| Publication status | Published - 2015 Oct 14 |
| Event | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States Duration: 2015 Jun 7 → 2015 Jun 12 |
Publication series
| Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| Volume | 07-12-June-2015 |
| ISSN (Print) | 1063-6919 |
Other
| Other | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 |
|---|---|
| Country | United States |
| City | Boston |
| Period | 15/6/7 → 15/6/12 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
Cite this
}
Multi-instance object segmentation with occlusion handling. / Chen, Yi Ting; Liu, Xiaokai; Yang, Ming Hsuan.
IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015. IEEE Computer Society, 2015. p. 3470-3478 7298969 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 07-12-June-2015).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Multi-instance object segmentation with occlusion handling
AU - Chen, Yi Ting
AU - Liu, Xiaokai
AU - Yang, Ming Hsuan
PY - 2015/10/14
Y1 - 2015/10/14
N2 - We present a multi-instance object segmentation algorithm to tackle occlusions. As an object is split into two parts by an occluder, it is nearly impossible to group the two separate regions into an instance by purely bottomup schemes. To address this problem, we propose to incorporate top-down category specific reasoning and shape prediction through exemplars into an intuitive energy minimization framework. We perform extensive evaluations of our method on the challenging PASCAL VOC 2012 segmentation set. The proposed algorithm achieves favorable results on the joint detection and segmentation task against the state-of-the-art method both quantitatively and qualitatively.
AB - We present a multi-instance object segmentation algorithm to tackle occlusions. As an object is split into two parts by an occluder, it is nearly impossible to group the two separate regions into an instance by purely bottomup schemes. To address this problem, we propose to incorporate top-down category specific reasoning and shape prediction through exemplars into an intuitive energy minimization framework. We perform extensive evaluations of our method on the challenging PASCAL VOC 2012 segmentation set. The proposed algorithm achieves favorable results on the joint detection and segmentation task against the state-of-the-art method both quantitatively and qualitatively.
UR - http://www.scopus.com/inward/record.url?scp=84959224495&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959224495&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2015.7298969
DO - 10.1109/CVPR.2015.7298969
M3 - Conference contribution
AN - SCOPUS:84959224495
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 3470
EP - 3478
BT - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
PB - IEEE Computer Society
ER -