Texture is one of the most important elements used by the human visual system (HVS) to distinguish different objects in a scene. Early bio-inspired methods for texture segmentation involve partitioning an image into distinct regions by setting a criterion based on their frequency response and local properties in order to further perform a grouping task. Nevertheless, the correct texture delimitation still remains as an important challenge in image segmentation. The aim of this study is to generate a novel approach to discriminate different textures by comparing internal and external image content in a set of evolving curves. We propose a multiphase formulation with an active contour model applied on the highest energy coefficients generated by the Hermite transform (HT). Local texture features such as scale and orientation are reflected in the HT coefficients which guide the evolution of each curve. This process leads to the enclosure of similar characteristics in a region associated with a level set function. The efficiency of our proposal is evaluated using a variety of synthetic images and real textured scenes.
Periodic variations in patterns within a group of pixels provide important information about the surface of interest and can be used to identify objects or regions. Hence, a proper analysis can be applied to extract particular features according to some specific image properties. Recently, texture analysis using orthogonal polynomials has gained attention since polynomials characterize the pseudo-periodic behavior of textures through the projection of the pattern of interest over a group of kernel functions. However, the maximum polynomial order is often linked to the size of the texture, which implies in many cases, a complex calculation and introduces instability in higher orders leading to computational errors. In this paper, we address this issue and explore a pre-processing stage to compute the optimal size of the window of analysis called “texel.” We propose Haralick-based metrics to find the main oscillation period, such that, it represents the fundamental texture and captures the minimum information, which is sufficient for classification tasks. This procedure avoids the computation of large polynomials and reduces substantially the feature space with small classification errors. Our proposal is also compared against different fixed-size windows. We also show similarities between full-image representations and the ones based on texels in terms of visual structures and feature vectors using two different orthogonal bases: Tchebichef and Hermite polynomials. Finally, we assess the performance of the proposal using well-known texture databases found in the literature.