Figure - PMC

Skip to main content

View full-text article in PMC

. Author manuscript; available in PMC: 2011 Nov 3.

Published in final edited form as: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2011 Jun 20;2011:1057–1064. doi: 10.1109/CVPR.2011.5995561

Feature-guided seam matching in the moving image. The correspondences in I and I′ are shown with the colored crosses. From (a) we can see the seams S_I″ and S_I′ may have same cost values due to the similar structure they passed. Obviously, S_I″ is not correct. We can use the matched features to guide the seam matching. For the point p ∈S_I, the features in a local region form a structure topology of p that help to reject incorrect matching. An example matched seam in the moving image is showed in (b). where denotes the neighboring system and β is the parameter to balance the two terms. Suppose label l_p is defined by the displacement d_p. let ΔII′ be (I(p − I′(p + d_p))². The data term for each node on the interface seam S is defined, as follows.

Inline graphic — Feature-guided seam matching in the moving image. The correspondences in I and I′ are shown with the colored crosses. From (a) we can see the seams S_I″ and S_I′ may have same cost values due to the similar structure they passed. Obviously, S_I″ is not correct. We can use the matched features to guide the seam matching. For the point p ∈S_I, the features in a local region form a structure topology of p that help to reject incorrect matching. An example matched seam in the moving image is showed in (b). where denotes the neighboring system and β is the parameter to balance the two terms. Suppose label l_p is defined by the displacement d_p. let ΔII′ be (I(p − I′(p + d_p))². The data term for each node on the interface seam S is defined, as follows.