TY - GEN
T1 - Prior-based segmentation by projective registration and level sets
AU - Riklin-Raviv, Tammy
AU - Kiryati, Nahum
AU - Sochen, Nir
PY - 2005/12/1
Y1 - 2005/12/1
N2 - Object detection and segmentation 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 a challenge. Recent works address this problem by using comprehensive training data. Other approaches are applicable only to limited object classes or can only accommodate similarity transformations. We suggest a novel variational approach to prior-based segmentation, which accounts for planar projective transformation, using a single reference object. The prior shape is registered concurrently with the segmentation process, without point correspondence. The algorithm detects the object of interest and correctly extracts its boundaries. The homography between the two object views is accurately recovered as well. Extending the Chan-Vese level set frame-work, we propose a region-based segmentation functional that includes explicit representation of the projective homography between the prior shape and the shape to segment. The formulation is derived from two-view geometry. Segmentation of a variety of objects is demonstrated and the recovered transformation is verified.
AB - Object detection and segmentation 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 a challenge. Recent works address this problem by using comprehensive training data. Other approaches are applicable only to limited object classes or can only accommodate similarity transformations. We suggest a novel variational approach to prior-based segmentation, which accounts for planar projective transformation, using a single reference object. The prior shape is registered concurrently with the segmentation process, without point correspondence. The algorithm detects the object of interest and correctly extracts its boundaries. The homography between the two object views is accurately recovered as well. Extending the Chan-Vese level set frame-work, we propose a region-based segmentation functional that includes explicit representation of the projective homography between the prior shape and the shape to segment. The formulation is derived from two-view geometry. Segmentation of a variety of objects is demonstrated and the recovered transformation is verified.
UR - http://www.scopus.com/inward/record.url?scp=33745953792&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2005.192
DO - 10.1109/ICCV.2005.192
M3 - Conference contribution
AN - SCOPUS:33745953792
SN - 076952334X
SN - 9780769523347
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 204
EP - 211
BT - Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
T2 - Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Y2 - 17 October 2005 through 20 October 2005
ER -