Schematic illustration of Gabor Filter Bank (GFB) and Filter-Rectify-Filter (FRF) classifier models.

(a) GFB model. An image patch is analyzed by a bank of 72 oriented log-Gabor filters covering 3 spatial scales (8, 16, 32 pixels). The rectified filter outputs are downsampled and MAX pooled to obtain a set of regressors x1,…,xn. Binomial logistic regression is performed to find a set of weights that optimally predict the probability that the patch is an occlusion. (b) FRF model. This model is identical to the GFB model, except the regressors x1,…,xn are then passed into a three-layer neural network having K = 4, 6, 10 hidden units (gray circles) to determine the probability an image patch is an occlusion edge.