TY  - JOUR
AB  - Purpose– The purpose of this paper is to propose a set of techniques, in the domain of texture analysis, dedicated to the classification of industrial textures. One of the main purposes was to deal with a high diversity of textures, including structural and highly random patterns.Design/methodology/approach– The global system includes a texture segmentation phase and a classification phase. The approach for image texture segmentation is based on features extracted from wavelets transform, fuzzy spectrum and interaction maps. The classification architecture uses a fuzzy grammar inference system.Findings– The classifier uses the aggregation of features from the several segmentation techniques, resulting in high flexibility concerning the diversity of industrial textures. The resulted system allows on‐line learning of new textures. This approach avoids the need for a global re‐learning of the all textures each time a new texture is presented to the system.Practical implications– These achievements demonstrate the practical value of the system, as it can be applied to different industrial sectors for quality control operations.Originality/value– The global approach was integrated in a cork vision system, leading to an industrial prototype that has already been tested. Similarly, it was tested in a textile machine, for a specific fabric inspection, and gave results that corroborate the diversity of possible applications. The segmentation procedure reveals good performance that is indicated by high classification rates, revealing good perspectives for full industrialization.
VL  - 29
IS  - 2
SN  - 0260-2288
DO  - 10.1108/02602280910936273
UR  - https://doi.org/10.1108/02602280910936273
AU  - Ferreira Manuel
AU  - Santos Cristina
AU  - Monteiro Joao
ED  - Torben Lenau
PY  - 2009
Y1  - 2009/01/01
TI  - A texture segmentation prototype for industrial inspection applications based on fuzzy grammar
T2  - Sensor Review
PB  - Emerald Group Publishing Limited
SP  - 163
EP  - 173
Y2  - 2024/04/16
ER  -