A computational approach to boundary detection
A unified approach to boundary perception is presented. The model consists of a hierarchical system which extracts and groups salient features in the image at different spatial scales. In the first stage a Gabor wavelet decomposition provides a representation of the image which is orientation selective, has optimal localization properties, and provides a good model for early feature detection. Following this, local competitive interactions are introduced which help in reducing the effects of noise and illumination variations. Scale interactions help in localizing line ends and corners, and play an important role in boundary perception. The final stage groups similar features aiding in boundary completion. Experimental results on detecting edges, texture boundaries, and illusory contours are provided.<<ETX>>