Natural Material Recognition with Illumination Invariant Textural Features

Abstract

A visual appearance of natural materials fundamentally depends on illumination conditions, which significantly complicates a real scene analysis. We propose textural features based on fast Markovian statistics, which are simultaneously invariant to illumination colour and robust to illumination direction. No knowledge of illumination conditions is required and a recognition is possible from a single training image per material. Material recognition is tested on the currently most realistic visual representation -Bidirectional Texture Function (BTF), using the Amsterdam Library of Textures (ALOT), which contains 250 natural materials acquired in different illumination conditions. Our proposed features significantly outperform several leading alternatives including Local Binary Patterns (LBP, LBP-HF) and Gabor features.

Texture recognition by using a non-linear kernel

“…A large number of feature extraction methods have been proposed and tested in various pattern recognition tasks. Vácha and Haindl (2010) proposed a statistical scheme for recognising three-dimensional textures shown in motion images. Vácha and Haindl (2010) proposed a statistical scheme for recognising three-dimensional textures shown in motion images.…”

Material recognition by feature classification using time-of-flight camera

“…Over the years, many works have been proposed to achieve material classification. [1][2][3][4][5][6] Most of the common approaches are based on color analysis or textural appearance. In this regard, a method based on 3-D textons 1 was introduced to recognize surfaces on the basis of their textural appearance.…”