Suppose T = { 5 , 9 , 1 , 10 , 15 } {\displaystyle T=\lbrace 5,9,1,10,15\rbrace } and C = { 20 , 4 , 7 , 13 , 19 , 11 } {\displaystyle C=\lbrace 20,4,7,13,19,11\rbrace } then
Hence, in the above example the Control region was a little bit brighter than the Treatment region.
Since Ranklets are non-linear filters, they can only be applied in the spatial domain. 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. These ranking scores determine a pairwise comparison between the T and C regions. This means that a ranklet essentially counts the number of TxC pairs which are brighter in the T set. Hence a positive value means that the Treatment values are brighter than the Control values, and vice versa.
"www.Ranklets.net". www.eecs.qmul.ac.uk. Retrieved 2022-06-05. http://www.eecs.qmul.ac.uk/~fabri/www.ranklets.net/index.html ↩
Smeraldi, Fabrizio (2002). "Ranklets: Orientation Selective Non-Parametric Features Applied to Face Detection". 16th International Conference on Pattern Recognition, ICPR 2002, Quebec, Canada, August 11–15, 2002. IEEE Computer Society. pp. 379–382. doi:10.1109/ICPR.2002.1047924. https://hh.diva-portal.org/smash/record.jsf?pid=diva2%3A290965 ↩
"www.Ranklets.net". www.eecs.qmul.ac.uk. Retrieved 2022-06-05. http://www.eecs.qmul.ac.uk/~fabri/www.ranklets.net/nonZim/wilcoxon/computation.html ↩