The Localized Consistency Principle for Image Matching under Non-uniform Illumination Variation and Affine Distortion

This paper proposes an image matching method that is robust to illumination variation and affine distortion. Our idea is to do image matching through establishing an imaging function that describes the functional relationship relating intensity values between two images. Similar methodology has been proposed by Viola [11] and Lai & Fang [6]. Viola proposed to do image matching through establishment of an imaging function based on a consistency principle. Lai & Fang proposed a parametric form of the imaging function. In cases where the illumination variation is not globally uniform and the parametric form of imaging function is not obvious, one needs to have a more robust method. Our method aims to take care of spatially non-uniform illumination variation and affine distortion. Central to our method is the proposal of a localized consistency principle, implemented through a non-parametric way of estimating the imaging function. The estimation is effected through optimizing a similarity measure that is robust under spatially non-uniform illumination variation and affine distortion. Experimental results are presented from both synthetic and real data. Encouraging results were obtained.

[1]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1995, Proceedings of IEEE International Conference on Computer Vision.

[2]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[3]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[4]  Yair Weiss,et al.  Deriving intrinsic images from image sequences , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Venu Madhav Govindu,et al.  Alignment Using Distributions of Local Geometric Properties , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Jr. Thomas G. Stockham,et al.  Image processing in the context of a visual model , 1972 .

[7]  Kazuo Kyuma,et al.  Wavelet-domain principal component analysis applied to facial similarity trees, caricaturing, and nonlinear illumination-invariant processing , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[8]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, International Journal of Computer Vision.

[9]  Shang-Hong Lai,et al.  Robust and efficient image alignment with spatially varying illumination models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[10]  William L. Goffe.  SIMANN: A Global Optimization Algorithm using Simulated Annealing , 1996 .

[11]  P. Anandan,et al.  Robust multi-sensor image alignment , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[12]  Mutsuo Sano,et al.  A parametric template method and its application to robust matching , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).