Stereo and Specular Reflection
International Journal of Computer Vision
Correspondence with Cumulative Similiarity Transforms
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
A Radial Cumulative Similarity Transform for Robust Image Correspondence
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Non-rigid structure from motion using ranklet-based tracking and non-linear optimization
Image and Vision Computing
High-precision stereo disparity estimation using HMMF models
Image and Vision Computing
A nonparametric approach to face detection using ranklets
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Fast algorithms for the computation of Ranklets
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Color indexing by nonparametric statistics
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Robust image processing for an omnidirectional camera-based smart car door
ACM Transactions on Embedded Computing Systems (TECS)
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We present ordinal measures for establishing image correspondence. Linear correspondence measures like correlation and the sum of squared differences are known to be fragile. Ordinal measures, which are based on relative ordering of intensity values in windows, have demonstrable robustness to depth discontinuities, occlusion and noise. The relative ordering of intensity values in each window is represented by a rank permutation which is obtained by sorting the corresponding intensity data. By using a novel distance metric between the rank permutations, we arrive at ordinal correlation coefficients. These coefficients are independent of absolute intensity scale, i.e they are normalized measures. Further, since rank permutations are invariant to monotone transformations of the intensity values, the coefficients are unaffected by nonlinear effects like gamma variation between images. We have developed a simple algorithm for their efficient implementation. Experiments suggest the superiority of ordinal measures over existing techniques under non-ideal conditions. Though we present ordinal measures in the context of stereo, they serve as a general tool for image matching that is applicable to other vision problems such as motion estimation and image registration.