Contour detection based on multi-scale spatial inhibition and contextual modulation

A multi-scale processing model for contour detection based on the perception mechanism of primary visual cortex (V1) is proposed. At first, responses of V1 cells to contours and orientations are simulated by multi-scale Gabor filters, and multi-scale basic contours are obtained by filter vectors. Then, a three-dimensional (3D) DOG filter extended from its 2D counterpart is used to convolute with the multi-scale basic contours to accomplish inter-scale and intra-scale inhibition. After that, the inhibition results and orientations of multi-scale are combined respectively to obtain integrated contours and integrated orientations. Finally, contextual modulation based on both inhibition and facilitation fields is performed on the nonlinear mapping of integrated contours, and it also considers the weighting of distance and orientation difference, and enhances the colinearity and texture inhibition. Experiments on natural images demonstrate that the proposed model can improve the overall performance effectively in terms of both noise inhibition and accurate contour detection compared with other methods.