Name
Abstract | ||
|---|---|---|
Research on the quality assurance of textiles has been a subject of much interest, particularly in relation to defect detection and the classification of woven fibers. Known systems require the fabric to be flat and spread-out on 2D surfaces in order for it to be classified. Unlike other systems, this system is able to classify textiles when they are presented in piles and in assembly-line like environments. Technical approaches have been selected under the aspects of speed and accuracy using 2D camera image data. A patch-based solution was chosen using an entropy-based pre-selection of small image patches. Interest points as well as texture descriptors combined with principle component analysis were part of this evaluation. The results showed that a classification of image patches resulted in less computational cost but reduced accuracy by 3.67%. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005969300990105 | SIGMAP |
Keywords | Field | DocType |
Quality Assurance,Textile Fabrics,Pattern Recognition,Textile Classification | Computer vision,Computer science,Textile,Artificial intelligence,Camera image,Principal component analysis,Quality assurance | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dirk Siegmund | 1 | 2 | 3.43 |
| Olga Kähm | 2 | 9 | 0.87 |
| David Handtke | 3 | 0 | 0.68 |