A study on topological descriptors for the analysis of 3D surface texture

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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.

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论文评审过程:Received 10 November 2016, Revised 14 June 2017, Accepted 20 October 2017, Available online 27 October 2017, Version of Record 26 February 2018.

论文官网地址:https://doi.org/10.1016/j.cviu.2017.10.012