Ranklet

[1] Ranklets achieve similar response to Haar wavelets as they share the same pattern of orientation-selectivity, multi-scale nature and a suitable notion of completeness.

Rank-based (non-parametric) features have become popular in the field of image processing for their robustness in detecting outliers and invariance to monotonic transformations such as brightness, contrast changes and gamma correction.

It is a non-parametric alternative to the t-test used to test the hypothesis for the comparison of two independent distributions.

Filtering with Ranklets involves dividing an image window W into Treatment and Control regions as shown in the image below: Subsequently, Wilcoxon rank-sum test statistics are computed in order to determine the intensity variations among conveniently chosen regions (according to the required orientation) of the samples in W. The intensity values of both regions are then replaced by the respective ranking scores.

This means that a ranklet essentially counts the number of TxC pairs which are brighter in the T set.