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Proceedings Paper

Robustness of texture parameters for color texture analysis
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Paper Abstract

This article proposes to deal with noisy and variable size color textures. It also proposes to deal with quantization methods and to see how such methods change final results. The method we use to analyze the robustness of the textures consists of an auto-classification of modified textures. Texture parameters are computed for a set of original texture samples and stored into a database. Such a database is created for each quantization method. Textures from the set of original samples are then modified, eventually quantized and classified according to classes determined from a precomputed database. A classification is considered incorrect if the original texture is not retrieved. This method is tested with 3 textures parameters: auto-correlation matrix, co-occurrence matrix and directional local extrema as well as 3 quantization methods: principal component analysis, color cube slicing and RGB binary space slicing. These two last methods compute only 3 RGB bands but could be extended to more. Our results show that, with or without quantization, autocorrelation matrix parameter is less sensitive to noise and to scaling than the two other tested texture parameters. This implies that autocorrelation matrix should probably be preferred for texture analysis with non controlled condition, typically industrial applications where images could be noisy. Our results also shows that PCA quantization does not change results where the two other quantization methods change them dramatically.

Paper Details

Date Published: 9 February 2006
PDF: 10 pages
Proc. SPIE 6070, Machine Vision Applications in Industrial Inspection XIV, 607006 (9 February 2006); doi: 10.1117/12.643021
Show Author Affiliations
Ambroise Marin, Le2I, Univ. of Burgundy (France)
Gjøvik Univ. College (Norway)
David Connah, Gjøvik Univ. College (Norway)
Audrey Roman, Le2I, Univ. of Burgundy (France)
Jon Y. Hardeberg, Gjøvik Univ. College (Norway)
Pierre Gouton, Le2I, Univ. of Burgundy (France)


Published in SPIE Proceedings Vol. 6070:
Machine Vision Applications in Industrial Inspection XIV
Fabrice Meriaudeau; Kurt S. Niel, Editor(s)

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