Combining gray level and gradient magnitude for scene matching

A correlation algorithm for scene matching by using two intensity features, i.e., gray level and gradient magnitude, is proposed. The correlation surface produced by the algorithm is the simple summation of the two ones obtained by using gray level and gradient magnitude respectively with zero mean normalized cross correlation similarity measure. We show that theoretically the proposed method will give better matching performance compared to the correlation algorithm by either gray level or gradient magnitude only under some conditions. Experimental results with real images demonstrate that the new algorithm can even report better results than classical algorithms and some newly presented ones.

[1]  D. P. Huijsmans,et al.  CONTENT-BASED INDEXING PERFORMANCE: A CLASS SIZE NORMALIZED PRECISION, RECALL, GENERALITY EVALUATION , 2003 .

[2]  Robyn A. Owens,et al.  A New Metric for Grey-Scale Image Comparison , 1997, International Journal of Computer Vision.

[3]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  C.C. Teoh,et al.  Extraction of infrastructure details from fused image , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[5]  Martin C. Martin,et al.  Genetic programming for real world robot vision , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  William J. Christmas,et al.  Orientation Correlation , 2002, BMVC.

[7]  Narendra Ahuja,et al.  Matching Point Features with Ordered Geometric, Rigidity, and Disparity Constraints , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Chin-Chuan Han,et al.  Personal authentication using palm-print features , 2003, Pattern Recognit..

[9]  Giorgio Bonmassar,et al.  Improved cross-correlation for template matching on the Laplacian pyramid , 1998, Pattern Recognit. Lett..

[10]  Azriel Rosenfeld,et al.  Point pattern matching by relaxation , 1980, Pattern Recognit..

[11]  Michael H. F. Wilkinson,et al.  Optimizing Edge Detectors for Robust Automatic Threshold Selection: Coping with Edge Curvature and Noise , 1998, Graph. Model. Image Process..

[12]  Trevor Darrell,et al.  Efficient image matching with distributions of local invariant features , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).