TY - JOUR
T1 - Prior-based segmentation and shape registration in the presence of perspective distortion
AU - Riklin-Raviv, Tammy
AU - Kiryati, Nahum
AU - Sochen, Nir
N1 - Funding Information:
This research was supported by MUSCLE: Multimedia Understanding through Semantics, Computation and Learning, a European Network of Excellence funded by the EC 6th Framework IST Programme.
PY - 2007/5
Y1 - 2007/5
N2 - Challenging object detection and segmentation tasks can be facilitated by the availability of a reference object. However, accounting for possible transformations between the different object views, as part of the segmentation process, remains difficult. Recent statistical methods address this problem by using comprehensive training data. Other techniques can only accommodate similarity transformations. We suggest a novel variational approach to prior-based segmentation, using a single reference object, that accounts for planar projective transformation. Generalizing the Chan-Vese level set framework, we introduce a novel shape-similarity measure and embed the projective homography between the prior shape and the image to segment within a region-based segmentation functional. The proposed algorithm detects the object of interest, extracts its boundaries, and concurrently carries out the registration to the prior shape. We demonstrate prior-based segmentation on a variety of images and verify the accuracy of the recovered transformation parameters.
AB - Challenging object detection and segmentation tasks can be facilitated by the availability of a reference object. However, accounting for possible transformations between the different object views, as part of the segmentation process, remains difficult. Recent statistical methods address this problem by using comprehensive training data. Other techniques can only accommodate similarity transformations. We suggest a novel variational approach to prior-based segmentation, using a single reference object, that accounts for planar projective transformation. Generalizing the Chan-Vese level set framework, we introduce a novel shape-similarity measure and embed the projective homography between the prior shape and the image to segment within a region-based segmentation functional. The proposed algorithm detects the object of interest, extracts its boundaries, and concurrently carries out the registration to the prior shape. We demonstrate prior-based segmentation on a variety of images and verify the accuracy of the recovered transformation parameters.
KW - Homography
KW - Level-sets
KW - Prior-based segmentation
KW - Projective transformation
KW - Registration
KW - Variational methods
UR - http://www.scopus.com/inward/record.url?scp=33846197950&partnerID=8YFLogxK
U2 - 10.1007/s11263-006-9042-y
DO - 10.1007/s11263-006-9042-y
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AN - SCOPUS:33846197950
SN - 0920-5691
VL - 72
SP - 309
EP - 328
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
IS - 3
ER -