@inproceedings{64864b42231c48d3b6a67d579c60611e,
title = "Defect Detection in Textiles with Co-occurrence Matrix as a Texture Model Description",
abstract = "Automatized inspection at textile production lines becomes very important. However, there is still a need to design methods which meet not only demands concerning accuracy of defect detection, but also ones related to the processing time. In this work, a novel approach for defect model definition is presented. It is derived from the idea of co-occurrence matrix. Due to scale incorporation and binarization of the model content it proved to be a very powerful descriptor of the novelties. Moreover, it also satisfies the requirements of short processing time. The defect mask achieved with the introduced method was compared visually to other popular solutions and show a very high accuracy and quality of defect description. The processing time is real-time as the response for a 1MP (megapixel) image is reached within tens of milliseconds.",
keywords = "Co-occurrence matrix, Defect detection, Image segmentation",
author = "Karolina Nurzynska and Micha{\l} Czardybon",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 19th International Workshop on Combinatorial Image Analysis, IWCIA 2018 ; Conference date: 22-11-2018 Through 24-11-2018",
year = "2018",
doi = "10.1007/978-3-030-05288-1\_17",
language = "English",
isbn = "9783030052874",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "216--226",
editor = "Barneva, \{Reneta P.\} and Tavares, \{Jo{\~a}o Manuel R.S\} and Brimkov, \{Valentin E.\}",
booktitle = "Combinatorial Image Analysis - 19th International Workshop, IWCIA 2018, Proceedings",
address = "Germany",
}