ABSTRACT
The quality control is an essential step in fabric industries. Detect
defects in the early stages can reduce costs and increase the quality
of the products. Currently, this task is mainly done by humans,
whose judgment can be affected by fatigue. Computer vision-based
techniques can automatically detect defects, reducing the need for
human intervention. In this context, this work proposes an image
block-processing approach, where we compare the Segmentation-
Based Fractal Texture Analysis, Gray Level Co-Occurrence Matrix,
and Local Binary Pattern in the feature extraction step. Aiming
to show the efficiency of this approach for the problem, these results
were compared with the same algorithms without the blockprocessing
approach. A Support Vector Machine optimized by Grid-
Search Algorithm was used to classify the fabrics. The database
used, which is available online, is composed of 479 images from
samples with defects and without it. The results show that this
block processing approach can improve the classification results,
achieving 100% in this work.