Adaptive Robust Structure Tensors for Orientation Estimation and Image Segmentation

Abstract

Recently, Van Den Boomgaard and Van De Weijer have presented an algorithm for texture analysis using robust tensor-based estimation of orientation. Structure tensors are a useful tool for reliably estimating oriented structures within a neighborhood and in the presence of noise. In this paper, we extend their work by using the Geman-McClure robust error function and, developing a novel iterative scheme that adaptively and simultaneously, changes the size, orientation and weighting of the neighborhood used to estimate the local structure tensor. The iterative neighborhood adaptation is initialized using the total least-squares solution for the gradient using a relatively large isotropic neighborhood. Combining our novel region adaptation algorithm, with a robust tensor formulation leads to better localization of low-level edge and junction image structures in the presence of noise. Preliminary results, using synthetic and biological images are presented.