@article{zeppelzauer_study_2018, title = {A {Study} on {Topological} {Descriptors} for the {Analysis} of {3D} {Surface} {Texture}}, volume = {167}, issn = {1077-3142}, url = {https://arxiv.org/pdf/1710.10662}, doi = {10/ghpp2h}, abstract = {Methods 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. Results show that class-specific information is reflected well in topological descriptors. The investigated descriptors can directly compete with non-topological descriptors and capture orthogonal information. Moreover they improve the state-of-the-art in combination with non-topological descriptors.}, journal = {Journal on Computer Vision and Image Understanding (CVIU)}, author = {Zeppelzauer, Matthias and Zielinski, Bartosz and Juda, Mateusz and Seidl, Markus}, year = {2018}, note = {Projekt: PITOTI 3D}, keywords = {3D surface classification, Center for Artificial Intelligence, Computer Vision, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Surface texture analysis, Visual Computing, Wiss. Beitrag, best, best-lbseidl, best-mzeppelzauer, peer-reviewed, persistence diagram, persistence image, persistent homology, surface representation, surface topology analysis}, pages = {74 -- 88}, }