A Study on Topological Descriptors for the Analysis of 3D Surface Texture
This work addresses texture classification in computer vision, offering an incremental improvement by integrating topological descriptors with existing techniques.
The study tackled the problem of classifying 3D surface textures by investigating topological descriptors, finding that they reflect class-specific information well and improve state-of-the-art results when combined with non-topological methods.
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 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.