Mahalanobis Distance Cross-Correlation for Illumination-Invariant Stereo Matching

A robust similarity measure called the Mahalanobis distance cross-correlation (MDCC) is proposed for illumination-invariant stereo matching, which uses a local color distribution within support windows. It is shown that the Mahalanobis distance between the color itself and the average color is preserved under affine transformation. The MDCC converts pixels within each support window into the Mahalanobis distance transform (MDT) space. The similarity between MDT pairs is then computed using the cross-correlation with an asymmetric weight function based on the Mahalanobis distance. The MDCC considers correlation on cross-color channels, thus providing robustness to affine illumination variation. Experimental results show that the MDCC outperforms state-of-the-art similarity measures in terms of stereo matching for image pairs taken under different illumination conditions.

[1]  Shree K. Nayar,et al.  Ordinal Measures for Image Correspondence , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Jonathan M. Garibaldi,et al.  Real-Time Correlation-Based Stereo Vision with Reduced Border Errors , 2002, International Journal of Computer Vision.

[3]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  S. Chambon,et al.  EVALUATION OF DIFFERENT METHODS FOR USING COLOUR INFORMATION IN GLOBAL STEREO MATCHING APPROACHES , 2008 .

[5]  Ruigang Yang,et al.  BRDF Invariant Stereo Using Light Transport Constancy , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Bernt Schiele,et al.  Comprehensive Colour Image Normalization , 1998, ECCV.

[7]  Cheng-Chew Lim,et al.  Segmentation of the face and hands in sign language video sequences using color and motion cues , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Vladimir Kolmogorov,et al.  Visual correspondence using energy minimization and mutual information , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  Gauthier Lafruit,et al.  Cross-Based Local Stereo Matching Using Orthogonal Integral Images , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[11]  Laurent Moll,et al.  Real time correlation-based stereo: algorithm, implementations and applications , 1993 .

[12]  Sang Uk Lee,et al.  Robust Stereo Matching Using Adaptive Normalized Cross-Correlation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Alain Trémeau,et al.  Affine transforms between image space and color space for invariant local descriptors , 2013, Pattern Recognit..

[14]  Reinhard Männer,et al.  Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation , 2004, International Journal of Computer Vision.

[15]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[16]  Takeo Kanade,et al.  Bayesian color constancy for outdoor object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[17]  Larry H. Matthies,et al.  Enhanced real-time stereo using bilateral filtering , 2004, Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004..

[18]  Geoffrey Egnal,et al.  Mutual Information as a Stereo Correspondence Measure , 2000 .

[19]  Vladimir Kolmogorov,et al.  Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Andrew Zisserman,et al.  Viewpoint invariant texture matching and wide baseline stereo , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[21]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Feiping Nie,et al.  Learning a Mahalanobis distance metric for data clustering and classification , 2008, Pattern Recognit..

[24]  Nicholas Costen,et al.  How Should We RepresentFaces for Automatic Recognition? , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Gerald Schaefer,et al.  Illuminant and device invariant colour using histogram equalisation , 2005, Pattern Recognit..

[27]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Mark D. Fairchild,et al.  Color Appearance Models , 1997, Computer Vision, A Reference Guide.

[29]  Yiannis Aloimonos,et al.  Robust Contrast Invariant Stereo Correspondence , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[30]  Luc Van Gool,et al.  Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions , 2000, BMVC.

[31]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[32]  William T. Freeman,et al.  Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[33]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields , 2006, ECCV.

[34]  Luc Florack,et al.  Using Top-Points as Interest Points for Image Matching , 2005, DSSCV.

[35]  M. Bleyer.  Does Color Really Help in Dense Stereo Matching ? , 2010 .

[36]  Heiko Hirschmüller,et al.  Evaluation of Stereo Matching Costs on Images with Radiometric Differences , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.