TY - GEN
T1 - Convex shape prior for multi-object segmentation using a single level set function
AU - Luo, Shousheng
AU - TAI, Xue-Cheng
AU - Huo, Limei
AU - Wang, Yang
AU - GLOWINSKI, Roland
N1 - Funding Information:
S. Luo was supported by the Programs for Science and Technology Development of Henan Province (192102310181). X. C. Tai was supported by the startup grant at Hong Kong Baptist University, grants RG(R)-RC/17-18/02-MATH and FRG2/17-18/033. Y. Wang was supported in part by the Hong Kong Research Grant Council grants 16306415 and 16308518. R. Glowinski was supported by the Hong Kong Baptist University and by the Kennedy Wong Foundation.
PY - 2019/10
Y1 - 2019/10
N2 - Many objects in real world have convex shapes. It is a difficult task to have representations for convex shapes with good and fast numerical solutions. This paper proposes a method to incorporate convex shape prior for multi-object segmentation using level set method. The relationship between the convexity of the segmented objects and the signed distance function corresponding to their union is analyzed theoretically. This result is combined with Gaussian mixture method for the multiple objects segmentation with convexity shape prior. Alternating direction method of multiplier (ADMM) is adopted to solve the proposed model. Special boundary conditions are also imposed to obtain efficient algorithms for 4th order partial differential equations in one step of ADMM algorithm. In addition, our method only needs one level set function regardless of the number of objects. So the increase in the number of objects does not result in the increase of model and algorithm complexity. Various numerical experiments are illustrated to show the performance and advantages of the proposed method.
AB - Many objects in real world have convex shapes. It is a difficult task to have representations for convex shapes with good and fast numerical solutions. This paper proposes a method to incorporate convex shape prior for multi-object segmentation using level set method. The relationship between the convexity of the segmented objects and the signed distance function corresponding to their union is analyzed theoretically. This result is combined with Gaussian mixture method for the multiple objects segmentation with convexity shape prior. Alternating direction method of multiplier (ADMM) is adopted to solve the proposed model. Special boundary conditions are also imposed to obtain efficient algorithms for 4th order partial differential equations in one step of ADMM algorithm. In addition, our method only needs one level set function regardless of the number of objects. So the increase in the number of objects does not result in the increase of model and algorithm complexity. Various numerical experiments are illustrated to show the performance and advantages of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=85081908567&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2019.00070
DO - 10.1109/ICCV.2019.00070
M3 - Conference contribution
AN - SCOPUS:85081908567
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 613
EP - 621
BT - Proceedings - 2019 International Conference on Computer Vision, ICCV 2019
PB - IEEE
T2 - 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
Y2 - 27 October 2019 through 2 November 2019
ER -