How effective is a rank-based filter with a frequency and orientation selective response?

Abstract

The pioneering study about the functions of the primary visual cortex by Hubel and Wiesel, motivated scientists to design machine vision models which tentatively simulate the same functions. In visual perception, there are two kinds of stimuli, usually referred to as first-order stimuli, characterized by a difference in luminance (the object tends to be lighter or darker than the background) and second-order stimuli that are characterized by difference in texture (the object and background share the same luminance but differ in texture). The literature shows that the acceptable models for first- and second-order stimuli are 2-D Gabor linear filters and the linear-nonlinear-linear (LNL) approach respectively. However, this is still not entirely satisfactory because first- and second-order stimuli are processed through different channels, which require an a priori knowledge of the nature of the image; this is not plausible either from the perceptual or from the application point of view. The existing orientation-selective, nonparametric features, called ranklets, which are based on the computation of Wilcoxon rank-sum test statistics has motivated us to investigate the applicability of other rank statistics which would be suitable for both kinds of stimuli. This study proposes an innovative rank-based filter, with orientation- and frequencyselective response. The filter is based on an approximation of a 2-D Gabor filter and on a combination of the Wilcoxon (location) and the Siegel-Tukey (dispersion) nonparametric statistics. The promising results obtained from experiments on perceptual stimuli show that the proposed filter is sensitive to both first- and second-order stimuli.