Texture segmentation using a class of narrowband filters
A class of 2D filters is proposed for segmenting visible images into regions of uniform texture. The filters used, known as Gabor filters, are optimal in several senses: they have tunable orientation bandwidths, they can be defined to operate over a range of spatial frequency channels, and they obey the uncertainty principle in two dimensions. The filters are interpreted as transforming the image into a modulated narrowband signal whose envelope coincides with the textured region to which the filter is tuned. Moreover, the receptive fields of neurons in the visual cortex are known to have shapes that approximate 2D Gabor filters, whose purpose has been uncertain. We suggest that they may play an important role in texture segmentation/surface perception. The technique is demonstrated using a variety of natural and synthetic textures.