Modeling second-order boundary perception: A machine learning approach
Fig 2
Neural network model implementing a FRF (Filter-Rectify-Filter) arrangement for texture segmentation.
Parameters shown in blue are learned from data, those in black are fixed. Stimulus image I is filtered by a bank of first-stage energy filters, resembling V1 complex cells, whose downsampled responses provide a feature vector input x to two second-stage filters. An optimization algorithm adjusts connection weights wL, wR (blue lines), producing second-stage filters which are selective for left-oblique (L) or right-oblique (R) contrast-defined boundaries, to give responses consistent with human psychophysical data. The output of each second-stage filter is passed through a nonlinearity h(u) = |u|α (blue curve) whose shape parameter α is also estimated from the psychophysical data. Fixed output weights vR = +1, vL = -1 lead to the decision variable u = sR − sL + v0, which is input to a sigmoid function to determine the probability of the observer classifying a given boundary as being right-oblique.