Fast algorithm for robust template matching with M-estimators

We propose a fast algorithm for speeding up the process of template matching that uses M-estimators for dealing with outliers. We propose a particular image hierarchy called the p-pyramid that can be exploited to generate a list of ascending lower bounds of the minimal matching errors when a nondecreasing robust error measure is adopted. Then, the set of lower bounds can be used to prune the search of the p-pyramid, and a fast algorithm is thereby developed in this paper. This fast algorithm ensures finding the global minimum of the robust template matching problem in which a nondecreasing M-estimator serves as an error measure. Experimental results demonstrate the effectiveness of our method.

[1]  Jan Flusser,et al.  Refined moment calculation using image block representation , 2000, IEEE Trans. Image Process..

[2]  Mohammed Ghanbari,et al.  The Cross-Search Algorithm for Motion Estimation , 1990 .

[3]  Xinhua Zhuang,et al.  Pose estimation from corresponding point data , 1989, IEEE Trans. Syst. Man Cybern..

[4]  M. GHANBARI,et al.  The cross-search algorithm for motion estimation [image coding] , 1990, IEEE Trans. Commun..

[5]  Azriel Rosenfeld,et al.  Robust regression methods for computer vision: A review , 1991, International Journal of Computer Vision.

[6]  Jiri Matas,et al.  Support vector machines for face authentication , 2002, Image Vis. Comput..

[7]  Charles V. Stewart,et al.  Robust Parameter Estimation in Computer Vision , 1999, SIAM Rev..

[8]  Yi-Ping Hung,et al.  Fast block matching algorithm based on the winner-update strategy , 2001, IEEE Trans. Image Process..

[9]  Michael J. Black,et al.  On the unification of line processes, outlier rejection, and robust statistics with applications in early vision , 1996, International Journal of Computer Vision.

[10]  Yann LeCun,et al.  Efficient Pattern Recognition Using a New Transformation Distance , 1992, NIPS.

[11]  Dong-Gyu Sim,et al.  Object matching algorithms using robust Hausdorff distance measures , 1999, IEEE Trans. Image Process..

[12]  Ming Lei Liou,et al.  Genetic motion search algorithm for video compression , 1993, IEEE Trans. Circuits Syst. Video Technol..

[13]  Ezzatollah Salari,et al.  Successive elimination algorithm for motion estimation , 1995, IEEE Trans. Image Process..

[14]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[15]  Chang-Hsing Lee,et al.  A fast motion estimation algorithm based on the block sum pyramid , 1997, IEEE Trans. Image Process..

[16]  T Koga,et al.  MOTION COMPENSATED INTER-FRAME CODING FOR VIDEO CONFERENCING , 1981 .

[17]  Charles V. Stewart,et al.  Robust hierarchical algorithm for constructing a mosaic from images of the curved human retina , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[18]  Ulrich Neumann,et al.  Vision-based pose computation: robust and accurate augmented reality tracking , 1999, Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR'99).

[19]  Shang-Hong Lai.  Robust Image Matching under Partial Occlusion and Spatially Varying Illumination Change , 2000, Comput. Vis. Image Underst..

[20]  M. Bierling,et al.  Displacement Estimation By Hierarchical Blockmatching , 1988, Other Conferences.

[21]  Mon Chau Shie,et al.  Fast, Robust Block Motion Estimation Using Simulated Annealing , 2000 .

[22]  Yoichi Sato,et al.  Object recognition using local EGI and 3D models with M-estimators , 1999, Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480).

[23]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[24]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[25]  Kai-Kuang Ma,et al.  A new diamond search algorithm for fast block-matching motion estimation , 2000, IEEE Trans. Image Process..

[26]  Hermann Ney,et al.  Learning of Variability for Invariant Statistical Pattern Recognition , 2001, ECML.

[27]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[28]  Jiun-Hung Chen,et al.  Fast Method for Robust Template Matching , 2001 .

[29]  Stuart Geman,et al.  Statistical methods for tomographic image reconstruction , 1987 .

[30]  Reinhard Klette,et al.  Computer vision - three-dimensional data from images , 1998 .

[31]  Hong-Yuan Mark Liao,et al.  Face + Hair + Shoulders + Background != Face , 1997 .

[32]  Søren I. Olsen,et al.  Epipolar Line Estimation , 1992, ECCV.

[33]  Ja-Ling Wu,et al.  A lightweight genetic block-matching algorithm for video coding , 1998, IEEE Trans. Circuits Syst. Video Technol..

[34]  Zhengyou Zhang,et al.  Parameter estimation techniques: a tutorial with application to conic fitting , 1997, Image Vis. Comput..

[35]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[36]  Jan Flusser,et al.  Combined Invariants to Linear Filtering and Rotation , 1999, Int. J. Pattern Recognit. Artif. Intell..

[37]  Kim L. Boyer,et al.  Performance evaluation of a class of M-estimators for surface parameter estimation in noisy range data , 1993, IEEE Trans. Robotics Autom..

[38]  Wen-Liang Hwang,et al.  Adaptive early jump-out technique for fast motion estimation in video coding , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[39]  W. R. Buckland,et al.  Outliers in Statistical Data , 1979 .

[40]  Lurng-Kuo Liu,et al.  A block-based gradient descent search algorithm for block motion estimation in video coding , 1996, IEEE Trans. Circuits Syst. Video Technol..

[41]  Hichem Frigui,et al.  A Robust Competitive Clustering Algorithm With Applications in Computer Vision , 1999, IEEE Trans. Pattern Anal. Mach. Intell..