Iqbal M. Dar

Automated pilling detection and fuzzy classification of textile fabrics

In the textile industry, the degree of fabric pilling is subjectively determined by human inspectors resulting in inconsistent quality control. The observed resistance to pilling is reported on an arbitrary scale ranging from No. 5 (no pillings) to No. 1 (very severe pilling). This paper presents a system and a methodology that counts the number of pillings on textile fabric samples automatically and classifies them into one of the pre-defined classes with repeatable accuracy while accounting for the human judgment by allowing the determination of the degree of confidence assigned to the samples membership in each class. The system consists of an apparatus; an imaging and data processing software procedure for counting the number of pillings; and a methodology for classifying the fabric samples into one of the pre-defined classes with repeatable accuracy while accounting for human judgment. A CCD camera is used to capture successive gray scale images of the fabric sample. A series of segmentation, Radon transform, morphological filtering, and detrending operations are applied to the fabric images to determine the true pilling count. The structuring element for the morphological operations is designed such that fuzz balls (which are not pillings) are filtered. Using fuzzy membership functions, the fabric pilling count is mapped to fabric pilling resistance rating. The system has been successfully tested on a large number of fabric samples with different shades and textures provided by the textile industry.

Automated vision-based quality control for electro-optical module manufacturing

The paper describes an inspection system architecture for electro-optical module inspection. The manufacturing of electro-optical modules involves assembly of electronic component and optical component mounts on a printed circuit board. The system consists of a controls layer and a machine vision layer. The controls layer manages seven axis positioning system. The machine vision layer performs automated inspection of the module at component level as well as at joint level. There are three operation modes of the system. Use-friendly graphic interfaces have been developed for system operation. The system can be programmed off-line for positioning information. Once the coordinates for various inspection locations are programmed the system automatically learns fuzzy classification prototypes. The prototypes are then utilized for module inspection. A module scanning algorithm has been developed that can detect and identify electronics or optical missing components. Fuzzy average square weighted Euclidean distance classifier has been used for missing component classification. Results are reported to demonstrate the missing component algorithm.