iccv iccv2013 iccv2013-283 iccv2013-283-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yi Wu, Yoshihisa Ijiri, Ming-Hsuan Yang
Abstract: Detecting and registering nonrigid surfaces are two important research problems for computer vision. Much work has been done with the assumption that there exists only one instance in the image. In this work, we propose an algorithm that detects and registers multiple nonrigid instances of given objects in a cluttered image. Specifically, after we use low level feature points to obtain the initial matches between templates and the input image, a novel high-order affinity graph is constructed to model the consistency of local topology. A hierarchical clustering approach is then used to locate the nonrigid surfaces. To remove the outliers in the cluster, we propose a deterministic annealing approach based on the Thin Plate Spline (TPS) model. The proposed method achieves high accuracy even when the number of outliers is nineteen times larger than the inliers. As the matches may appear sparsely in each instance, we propose a TPS based match growing approach to propagate the matches. Finally, an approach that fuses feature and appearance information is proposed to register each nonrigid surface. Extensive experiments and evaluations demonstrate that the proposed algorithm achieves promis- ing results in detecting and registering multiple non-rigid surfaces in a cluttered scene.
[1] S. Baker and I. Matthews. Lucas-Kanade 20 Years On: A Unifying Framework. IJCV, 56(3):221–255, 2004.
[2] A. Bartoli. Maximizing the Predictivity of Smooth Deformable Image Warps through Cross-Validation. JMIV, 31(2-3): 133–145, 2008.
[3] A. Bartoli and A. Zisserman. Direct Estimation of Non-Rigid Registrations. In BMVC, 2004.
[4] A. C. Berg, T. L. Berg, and J. Malik. Shape Matching and Object Recognition using Low Distortion Correspondences. In CVPR, 2005.
[5] F. L. Bookstein. Principal Warps: Thin-Plate Splines and The Decomposition of Deformations. PAMI, 11(6):567–585, 1989.
[6] F. Brunet, V. Gay-Bellile, A. Bartoli, N. Navab, and R. Malgouyres. Feature-Driven Direct Non-Rigid Image Registration. IJCV, 93(1):33–52, 2011.
[7] M. Chertok and Y. Keller. Efficient High Order Matching. PAMI, 32(12):2205–15, 2010. 11999988 (a)(b)(c)(d)(e)(f) Figure 4. (a) Performance of AOR with different numbers of inliers. (b) Performance of AOR with different outlier ratios. (c) Comparison of AOR and LPOR. (d) Comparison of AOR and FAOR. (e) Matches before outlier rejection. (f) Refined matches by AOR. Figure 5. Detection and registration results on the home magazine images. Figure 6. Detection and registration results on the ID magazine images.
[8] M. Cho, J. Lee, and K. M. Lee. Feature Correspondence and Deformable Object Matching via Agglomerative Correspondence Clustering. In CVPR, 2009.
[9] M. Cho, Y. M. Shin, and K. M. Lee. Unsupervised Detection and Segmentation of Identical Objects. In CVPR, 2010.
[10] H. Chui and A. Rangarajan. A New Point Matching Algorithm for
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26] Non-Rigid Registration. CVIU, 89(2-3): 114–141, 2003. T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active Appearance Models. PAMI, 23(6):681–685, 2001. O. Duchenne, F. Bach, I.-S. Kweon, and J. Ponce. A Tensor-Based Algorithm for High-Order Graph Matching. PAMI, 33(12):2383– 2395, 2011. V. Ferrari, T. Tuytelaars, and L. V. Gool. Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views. IJCV, 67(2):159–188, 2006. M. Fischler and R. Bolles. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun. ACM, 24(6):381–395, 1981 . R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. 2000. M. Leordeanu and M. Hebert. A Spectral Technique for Correspondence Problems Using Pairwise Constraints. In ICCV, 2005. J. Lim and M.-H. Yang. A Direct Method for Modeling Non-Rigid Motion with Thin Plate Spline. In CVPR, 2005. D. G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. IJCV, 60(2):91–1 10, 2004. B. D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. In IJCAI, 1981. J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust Wide Baseline Stereo from Maximally Stable Extremal Regions. In ECCV, 2002. I. Matthews and S. Baker. Active Appearance Models Revisited. IJCV, 60(2): 135–164, 2004. D. Ok, R. Marlet, and J.-Y. Audibert. Efficient and Scalable 4th-order Match Propagation. In ACCV, 2012. J. Pilet, V. Lepetit, and P. Fua. Fast Non-Rigid Surface Detection, Registration and Realistic Augmentation. IJCV, 76(2): 109–122, 2008. D. Pizarro and A. Bartoli. Feature-Based Deformable Surface Detection with Self-Occlusion Reasoning. IJCV, 97(1):54–70, 2012. L. Torresani, V. Kolmogorov, and C. Rother. Feature Correspondence via GraphMatching: Models and Global Optimization. In ECCV, 2008. A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms, 2008.
[27] A. L. Yuille. Generalized Deformable Models, Statistical Physics, and Matching Problems. Neurul Computation, 24(1987): 1–24, 1990.
[28] R. Zass and A. Shashua. Probabilistic Graph and Hypergraph Matching. In CVPR, 2008.
[29] J. Zhu, M. R. Lyu, and T. S. Huang. A Fast 2D Shape Recovery Approach by Fusing Features and Appearance. PAMI, 31(7): 1210– 1224, 2009. 11999999