Details
| Original language | English |
|---|---|
| Title of host publication | Computer Vision, ACCV 2012 |
| Subtitle of host publication | 11th Asian Conference on Computer Vision, Revised Selected Papers |
| Pages | 745-759 |
| Number of pages | 15 |
| Edition | PART 1 |
| Publication status | Published - 2013 |
| Event | 11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of Duration: 5 Nov 2012 → 9 Nov 2012 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 1 |
| Volume | 7724 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
In this paper we propose an algorithm for image segmentation using graph cuts which can be used to efficiently solve labeling problems on high resolution images or image sequences. The basic idea of our method is to group large homogeneous regions to one single variable. Therefore we combine the appearance and the task specific similarity with Dempster's theory of evidence to compute the basic belief that two pixels/groups will have the same label in the minimum energy state. Experiments on image and video segmentation show that our grouping leads to a significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner with a low approximation loss.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Computer Vision, ACCV 2012: 11th Asian Conference on Computer Vision, Revised Selected Papers. PART 1. ed. 2013. p. 745-759 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7724 LNCS, No. PART 1).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Efficient pixel-grouping based on Dempster's theory of evidence for image segmentation
AU - Scheuermann, Björn
AU - Schlosser, Markus
AU - Rosenhahn, Bodo
PY - 2013
Y1 - 2013
N2 - In this paper we propose an algorithm for image segmentation using graph cuts which can be used to efficiently solve labeling problems on high resolution images or image sequences. The basic idea of our method is to group large homogeneous regions to one single variable. Therefore we combine the appearance and the task specific similarity with Dempster's theory of evidence to compute the basic belief that two pixels/groups will have the same label in the minimum energy state. Experiments on image and video segmentation show that our grouping leads to a significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner with a low approximation loss.
AB - In this paper we propose an algorithm for image segmentation using graph cuts which can be used to efficiently solve labeling problems on high resolution images or image sequences. The basic idea of our method is to group large homogeneous regions to one single variable. Therefore we combine the appearance and the task specific similarity with Dempster's theory of evidence to compute the basic belief that two pixels/groups will have the same label in the minimum energy state. Experiments on image and video segmentation show that our grouping leads to a significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner with a low approximation loss.
UR - http://www.scopus.com/inward/record.url?scp=84875892067&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37331-2_56
DO - 10.1007/978-3-642-37331-2_56
M3 - Conference contribution
AN - SCOPUS:84875892067
SN - 9783642373305
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 745
EP - 759
BT - Computer Vision, ACCV 2012
T2 - 11th Asian Conference on Computer Vision, ACCV 2012
Y2 - 5 November 2012 through 9 November 2012
ER -