A Convolutional Neural Networks-Based Approach for Texture Directionality Detection.

ABSTRACT: The perceived texture directionality is an important, not fully explored image characteristic. In many applications texture directionality detection is of fundamental importance. Several approaches have been proposed, such as the fast Fourier-based method. We recently proposed a method based on the interpolated grey-level co-occurrence matrix (iGLCM), robust to image blur and noise but slower than the Fourier-based method. Here we test the applicability of convolutional neural networks (CNNs) to texture directionality detection. To obtain the large amount of training data required, we built a training dataset consisting of synthetic textures with known directionality and varying perturbation levels. Subsequently, we defined and tested shallow and deep CNN architectures. We present the test

SUBMITTER: Kociolek M 

PROVIDER: S-EPMC8778371 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature