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View Dependent Surface Material Recognition

  • Stanislav Mikeš
  • Michal HaindlEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11844)

Abstract

The paper presents a detailed study of surface material recognition dependence on the illumination and viewing conditions which is a hard challenge in a realistic scene interpretation. The results document sharp classification accuracy decrease when using usual texture recognition approach, i.e., small learning set size and the vertical viewing and illumination angle which is a very inadequate representation of the enormous material appearance variability. The visual appearance of materials is considered in the state-of-the-art Bidirectional Texture Function (BTF) representation and measured using the upper-end BTF gonioreflectometer. The materials in this study are sixty-five different wood species. The supervised material recognition uses the shallow convolutional neural network (CNN) for the error analysis of angular dependency. We propose a Gaussian mixture model-based method for robust material segmentation.

Notes

Acknowledgments

The Czech Science Foundation project GAČR 19-12340S supported this research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The Institute of Information Theory and Automation of the Czech Academy of SciencesPragueCzechia

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