Siegmund, Dirk ; Kuijper, Arjan ; Braun, Andreas (2016)
Stereo-Image Normalization of Voluminous Objects Improves Textile Defect Recognition.
Advances in Visual Computing. 12th International Symposium, ISVC 2016. Las Vegas, NV, USA (December 12-14, 2016)
doi: 10.1007/978-3-319-50835-1_17
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The visual detection of defects in textiles is an important application in the textile industry. Existing systems require textiles to be spread flat so they appear as 2D surfaces, in order to detect defects. In contrast, we show classification of textiles and textile feature extraction methods, which can be used when textiles are in inhomogeneous, voluminous shape. We present a novel approach on image normalization to be used in stain-defect recognition. The acquired database consist of images of piles of textiles, taken using stereo vision. The results show that a simple classifier using normalized images outperforms other approaches using machine learning in classification accuracy.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Siegmund, Dirk ; Kuijper, Arjan ; Braun, Andreas |
| Art des Eintrags: | Bibliographie |
| Titel: | Stereo-Image Normalization of Voluminous Objects Improves Textile Defect Recognition |
| Sprache: | Englisch |
| Publikationsjahr: | Dezember 2016 |
| Verlag: | Springer International Publishing |
| Buchtitel: | Advances in Visual Computing |
| Reihe: | Lecture Notes in Computer Science (LNCS); 10072 |
| Veranstaltungstitel: | Advances in Visual Computing. 12th International Symposium, ISVC 2016 |
| Veranstaltungsort: | Las Vegas, NV, USA |
| Veranstaltungsdatum: | December 12-14, 2016 |
| DOI: | 10.1007/978-3-319-50835-1_17 |
| Kurzbeschreibung (Abstract): | The visual detection of defects in textiles is an important application in the textile industry. Existing systems require textiles to be spread flat so they appear as 2D surfaces, in order to detect defects. In contrast, we show classification of textiles and textile feature extraction methods, which can be used when textiles are in inhomogeneous, voluminous shape. We present a novel approach on image normalization to be used in stain-defect recognition. The acquired database consist of images of piles of textiles, taken using stereo vision. The results show that a simple classifier using normalized images outperforms other approaches using machine learning in classification accuracy. |
| Freie Schlagworte: | Guiding Theme: Digitized Work, Research Area: Computer vision (CV), 3D Image processing, Multi-view stereo, Machine learning, Pattern recognition |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 06 Mai 2019 07:25 |
| Letzte Änderung: | 07 Mai 2019 08:27 |
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