Vision-based inspection of industrial materials such as textile webs, paper, or wood requires the development of defect segmentation techniques based on texture analysis. In this work, a multi-channel filtering technique that imitates the early human vision process is applied to images captured on-line. This new approach uses Bernoulli's rule of combination for integrating images from different channels. Physical image size and yarn impurities are used as key parameters for tuning the sensitivity of the proposed algorithm. Several real fabric samples along with the result of segmented defects are presented. The results achieved show that developed algorithm is robust, scalable and computationally efficient for detection of local defects in textured materials.
|Number of pages||7|
|Journal||Conference Record - IAS Annual Meeting (IEEE Industry Applications Society)|
|Publication status||Published - 1 Dec 2000|
|Event||35th IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy - Rome, Italy|
Duration: 8 Oct 2000 → 12 Oct 2000
ASJC Scopus subject areas
- Electrical and Electronic Engineering