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Proceedings Paper

Stability of the metrological texture feature using colour contrast occurrence matrix
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Paper Abstract

Texture discrimination was studied a lot for texture classification/recognition in image databases, but less under the metrological point of view. In this work, we focused on the metrological behaviour related to the human vision for Control Quality purposes. Inside this study, we introduce as a pair a novel texture feature associated to an adapted similarity measure. The main idea was to define a compact representation adapted from the human visual characteristics in order to obtain an accurate description of the texture. Combined to an adapted similarity measure, the obtained pair feature/similarity becomes highly efficient. Performance Classification of the proposed texture feature is assessed on six popular and challenging databases used to provide the reference results in the state-of-the-art. Obtained results show the efficiency and the robustness of the proposed pair feature/similarity measure defined by the relocated Colour Contrast Occurrence Matrix.

Paper Details

Date Published: 16 July 2019
PDF: 8 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111721F (16 July 2019); doi: 10.1117/12.2521181
Show Author Affiliations
H. Jebali, Univ. of Tunis el Manar (Tunisia)
N. Richard, Univ. de Poitiers, XLIM-SIC, CNRS (France)
C. Fernandez-Maloigne, Univ. de Poitiers, XLIM-SIC, CNRS (France)
M. Naouai, Univ. of Tunis el Manar (Tunisia)

Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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