Calibrating second-moment matrix for better shape adaptation with bias term from directional filter bank

Abstract

Anisotropic diffusion is an important noise reduction process in many pattern recognition systems. The local orientations and shape are encoded in a descriptor, called second-moment matrix, to be used as the central part of the process. For a noisy image, it becomes hard to find the elements of the matrix with reasonable accuracy. Two layers of subsequent Gaussian smoothing are applied conventionally: One is referred to as noise scale and the other integration scale. It is still not guaranteed that a second-moment matrix is correctly aligned with underlying pattern flow directions. It is largely believed that the integration scale handles this orientation discrepancy. Therefore, some researchers suggested a space-dependent integration scale strategy. We propose here a calibration strategy using directional filterbank (DFB). The second-moment matrix is rotated to go along the proper ridge directions with an angle correction term from DFB. The experiments conducted show promise for the calibrated anisotropic diffusion process in terms of improved recognition rate over that of the un-calibrated process.