TitleA Study on Topological Descriptors for the Analysis of 3D Surface Texture
Publication TypeJournal Article
Year of Publication2018
AuthorsZeppelzauer, M., B. Zielinski, M. Juda, and M. Seidl
JournalComputer Vision and Image Understanding (CVIU)
Pages74 - 88
Date Published09/2017
Keywords3D surface classification, Deep Learning, Persistence diagram, persistence image, persistent homology, Surface representation, surface texture analysis, Surface topology analysis
AbstractMethods from computational topology are becoming more and more popular in computer vision and have shown to improve the state-of-the-art in several tasks. In this paper, we investigate the applicability of topological descriptors in the context of 3D surface analysis for the classification of different surface textures. We present a comprehensive study on topological descriptors, investigate their robustness and expressiveness and compare them with state-of-the-art methods including Convolutional Neural Networks (CNNs). Results show that class-specific information is reflected well in topological descriptors. The investigated descriptors can directly compete with non-topological descriptors and capture complementary information. As a consequence they improve the state-of-the-art when combined with non-topological descriptors.
Refereed DesignationRefereed