Contour detection by surround inhibition in the Circular Harmonic Functions domain

Standard edge detectors react to all not negligible luminance changes in an image, irrespective whether they are originated by object contours or by texture (e.g. grass, foliage, waves, etc.). Texture edges are often stronger than object contours, thus standard edge detectors fail in isolating object contours from texture. We propose a multiresolution contour detector, operating in the Circular Harmonic Function domain and motivated by biological principles. At each scale, texture is suppressed by using a bilateral surround inhibition process, applied after non-maxima suppression. The binary contour map is obtained by a contour-oriented thresholding algorithm, proved to be more effective than the classical hysteresis thresholding used in the Canny edge detector. Robustness to noise is achieved by a Bayesian gradient estimation.