New operators for optimized orientation estimation

Abstract

This paper focuses on directional textures. It provides a new framework for the design of convolution masks dedicated to orientation estimation. We propose a new technique based on the combination of two complementary operators: a gradient-based operator which is adapted to sloped regions and a valleyness detector which fits the crests and valleys. On each operator, a double optimization procedure is carried out with respect to bias and noise sensitivity reduction. The procedure is generic and applies to any kind of underlying directional texture. Experiments on a synthetic sine wave texture and on natural textures are provided and show the efficiency and the relevance of our approach.