Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Near-duplicate keyframe retrieval by semi-supervised learning and nonrigid image matching
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
3D structure refinement of nonrigid surfaces through efficient image alignment
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Real-time image mosaicing using non-rigid registration
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
A convolutional treelets binary feature approach to fast keypoint recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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In this paper, we present a fusion approach to solve the nonrigid shape recovery problem, which takes advantage of both the appearance information and the local features. We have two major contributions. First, we propose a novel progressive finite Newton optimization scheme for the feature-based nonrigid surface detection problem, which is reduced to only solving a set of linear equations. The key is to formulate the nonrigid surface detection as an unconstrained quadratic optimization problem that has a closed-form solution for a given set of observations. Second, we propose a deformable Lucas-Kanade algorithm that triangulates the template image into small patches and constrains the deformation through the second-order derivatives of the mesh vertices. We formulate it into a sparse regularized least squares problem, which is able to reduce the computational cost and the memory requirement. The inverse compositional algorithm is applied to efficiently solve the optimization problem. We have conducted extensive experiments for performance evaluation on various environments, whose promising results show that the proposed algorithm is both efficient and effective.