The ability to segment a textured image into separate regions (texture segmentation) continues to be a challenging problem in computer vision. Many texture-segmentation schemes are based on a filter-bank model, where the filters (henceforth referred to as Gabor Filters) are derived from Gabor elementary functions. The goal of these methods is to transform texture differences into detectable filter-output discontinuities at texture boundaries. Then, one can segment the image into differently textured regions. Distinct discontinuities occur, however, only if the parameters defining the Gabor filters are suitably chosen. Some previous analysis has shown how to design appropriate filters for discriminating simple textures. Designing filters for more general textures, though, has largely been done ad hoc. We have devised a new, more effective, more rigorously based method for determining Gabor-filter parameters. The method is based on an exhaustive, but efficient, search of Gabor-filter parameter space and on a detection-theory formulation of a Gabor filter's output. We provide qualitative arguments and experimental results indicating that our new method is more effective than other methods in producing suitable filter parameters. We demonstrate that our model also gives good filter designs for a variety of texture types.