Detecting subjective contours with the hierarchical Hough transform

Basic visual activities are related to edge detection (motion, recognition). We show that there are different kinds of edges. The most known and already studied are edges due to luminance variations. After a brief introduction to classical edge-detectors, we introduce another class of edges, the “subjective contours”. We assume end-points as textons as proposed by B. Julesz (1981) and D. Marr (1976). The first step of our algorithm consists in geometric feature (i.e. segments) extraction using the hierarchical Hough transform. The end-points of the detected features are then drawn on a new image. This second “feature map” (of higher level) is then processed in a second step using again the hierarchical Hough transform. The endpoints acts as clues indicating the presence of “subjective contours”. We show that there is a relation between the number of clues present in the “feature map” and the pop-out phenomenon of the “subjective contour”