Illuminant precompensation for texture discrimination using filters

Abstract

Texture-discrimination algorithms have often been tested on images containing either mosaics of synthetic textures or artificially created mosaics of real textures — in any case, images in which most of the changes in intensity can be ascribed to the textures themselves. However, real images are not formed like this and may contain steep gradations in intensity which have nothing to do with local texture, such as those caused by incident shadows. A texture discrimination algorithm based on linear filters can fail in the presence of these strong gradations, as they may easily contain an order of magnitude more energy than the gradations in intensity due to texture in the image per se. In these cases, the mechanism may become responsive only to strong luminance effects, and not to texture. I have found that good performance on natural images containing texture can only be obtained from a filter-based texture detection scheme if it includes a stage which attempts to bring large intensity gradients within bounds. The exact nature of the best precompensator appears to depend somewhat on the way the filter outputs are processed. The fit to psychophysical data and the implications for more detailed models of human texture processing will be discussed.