TY - GEN
T1 - Contour-constrained superpixels for image and video processing
AU - Lee, Se Ho
AU - Jang, Won Dong
AU - Kim, Chang-Su
N1 - Funding Information:
This work was supported partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2015R1A2A1A10055037), and partly by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2016-0-00464) supervised by the IITP (Institute for Information & communications Technology Promotion).
Funding Information:
This work was supported partly by the National Research Foundation of Korea(NRF) grant funded by the Koreagov-ernment (MSIP) (No. NRF-2015R1A2A1A10055037), and partly by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2016-0-00464) supervised by the IITP (Institute for Information & communications TechnologyPromotion).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - A novel contour-constrained superpixel (CCS) algorithm is proposed in this work. We initialize superpixels and regions in a regular grid and then refine the superpixel label of each region hierarchically from block to pixel levels. To make superpixel boundaries compatible with object contours, we propose the notion of contour pattern matching and formulate an objective function including the contour constraint. Furthermore, we extend the CCS algorithm to generate temporal superpixels for video processing. We initialize superpixel labels in each frame by transferring those in the previous frame and refine the labels to make superpixels temporally consistent as well as compatible with object contours. Experimental results demonstrate that the proposed algorithm provides better performance than the state-of-the-art superpixel methods.
AB - A novel contour-constrained superpixel (CCS) algorithm is proposed in this work. We initialize superpixels and regions in a regular grid and then refine the superpixel label of each region hierarchically from block to pixel levels. To make superpixel boundaries compatible with object contours, we propose the notion of contour pattern matching and formulate an objective function including the contour constraint. Furthermore, we extend the CCS algorithm to generate temporal superpixels for video processing. We initialize superpixel labels in each frame by transferring those in the previous frame and refine the labels to make superpixels temporally consistent as well as compatible with object contours. Experimental results demonstrate that the proposed algorithm provides better performance than the state-of-the-art superpixel methods.
UR - http://www.scopus.com/inward/record.url?scp=85041894131&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2017.621
DO - 10.1109/CVPR.2017.621
M3 - Conference contribution
AN - SCOPUS:85041894131
T3 - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
SP - 5863
EP - 5871
BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Y2 - 21 July 2017 through 26 July 2017
ER -