A model consisting of a multistage system which extracts and groups salient features in the image at different spatial scales (or frequencies) is used. In the first stage, a Gabor wavelet decomposition provides a representation of the image which is orientation selective and has optimal localization properties in space and frequency. This decomposition is useful in detecting significant features such as step and line edges at different scales and orientations in the image. Following the wavelet transformation, local competitive interactions are introduced to reduce the effects of noise and changes in illumination. Interscale interactions help in localizing the line ends and corners, and play a crucial role in boundary perception. The final stage groups similar features, aiding in boundary completion. The different stages can be identified with processing by simple, complex, and hypercomplex cells in the visual cortex of mammals. Experimental results demonstrate the performance of this model in detecting boundaries (both real and illusory) in real and synthetic images.