Neural network trained morphological processing for the detection of defects in woven fabric
| Autor: | Jayanta K. Chandra, Asit K. Datta, Pradipta K. Banerjee |
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| Rok vydání: | 2010 |
| Předmět: |
Engineering
Polymers and Plastics Artificial neural network business.industry Structuring element Materials Science (miscellaneous) Binary image Binary number Pattern recognition Machine learning computer.software_genre Thresholding Industrial and Manufacturing Engineering Woven fabric Sliding window protocol Dilation (morphology) Artificial intelligence General Agricultural and Biological Sciences business computer |
| Zdroj: | Journal of the Textile Institute. 101:699-706 |
| ISSN: | 1754-2340 0040-5000 |
| DOI: | 10.1080/00405000902812735 |
| Popis: | Basic morphological operations such as the erosion, dilation, opening, and closing often fail to detect various types of defects that may be present in woven fabric, mainly because of the heuristic selection of structuring element needed for these operations. In this paper, an artificial neural network (ANN) is utilized for the selection of structuring element, where ANN is trained by two pre‐assigned normalized numbers related to the warp and weft counts of the test fabric. The test gray fabric image is pre‐processed to remove noise and the interlaced grating structure of weft and warp and then converted to a binary image by thresholding. An intensity threshold value of the processed fabric image and the dimension of a sliding window needed for correlation operation are obtained from the trained ANN. Defects are detected after morphological reconstruction of the processed binary fabric image, where an ANN trained structuring element is used. The technique is tested on 317 samples for eight different type... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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