TY - GEN
T1 - Curvature regularity for region-based image segmentation and inpainting
T2 - 12th International Conference on Computer Vision, ICCV 2009
AU - Schoenemann, Thomas
AU - Kahl, Fredrik
AU - Cremers, Daniel
PY - 2009
Y1 - 2009
N2 - We consider a class of region-based energies for image segmentation and inpainting which combine region integrals with curvature regularity of the region boundary. To minimize such energies, we formulate an integer linear program which jointly estimates regions and their boundaries. Curvature regularity is imposed by respective costs on pairs of adjacent boundary segments. By solving the associated linear programming relaxation and thresholding the solution one obtains an approximate solution to the original integer problem. To our knowledge this is the first approach to impose curvature regularity in region-based formulations in a manner that is independent of initialization and allows to compute a bound on the optimal energy. In a variety of experiments on segmentation and inpainting, we demonstrate the advantages of higher-order regularity. Moreover, we demonstrate that for most experiments the optimality gap is smaller than 2% of the global optimum. For many instances we are even able to compute the global optimum.
AB - We consider a class of region-based energies for image segmentation and inpainting which combine region integrals with curvature regularity of the region boundary. To minimize such energies, we formulate an integer linear program which jointly estimates regions and their boundaries. Curvature regularity is imposed by respective costs on pairs of adjacent boundary segments. By solving the associated linear programming relaxation and thresholding the solution one obtains an approximate solution to the original integer problem. To our knowledge this is the first approach to impose curvature regularity in region-based formulations in a manner that is independent of initialization and allows to compute a bound on the optimal energy. In a variety of experiments on segmentation and inpainting, we demonstrate the advantages of higher-order regularity. Moreover, we demonstrate that for most experiments the optimality gap is smaller than 2% of the global optimum. For many instances we are even able to compute the global optimum.
UR - http://www.scopus.com/inward/record.url?scp=77953218055&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2009.5459209
DO - 10.1109/ICCV.2009.5459209
M3 - Conference contribution
AN - SCOPUS:77953218055
SN - 9781424444205
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 17
EP - 23
BT - 2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
Y2 - 29 September 2009 through 2 October 2009
ER -