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Conference Paper (international conference)

View Dependent Surface Material Recognition

Mikeš Stanislav, Haindl Michal

: Advances in Visual Computing : 14th International Symposium on Visual Computing (ISVC 2019), p. 156-167 , Eds: Bebis G., Boyle R., Parvin B., Koracin D.

: International Symposium on Visual Computing (ISVC 2019) /14./, (Lake Tahoe, US, 20191007)

: GA19-12340S, GA ČR

: convolutional neural network, texture recognition, Bidirectional Texture Function recognition

: 10.1007/978-3-030-33720-9_12

: http://library.utia.cas.cz/separaty/2019/RO/haindl-0510488.pdf

(eng): 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.

: BD

: 20205

2019-01-07 08:39