Perceptual grouping on texture images using non-cartesian networks

We present a two-stage approach, Curved Inertia Frames, to compute texture groups. In the first stage, we use a filter-based approach to estimate central texture group locations. We use existing bank filters but suggest a new filter post-processing operation. In the second stage, we compute global texture groups using the estimates of the first stage. This is done using dynamic programming on a novel non-cartesian network. Our scheme is novel because it does not compute texture edges as an intermediate step, it uses a new post-processing operation, and because it is guaranteed to find a global optimum due to the non-cartesian network used in the second stage