Robust Method for Detecting Defect in Images Printed on 3D Micro-textured Surfaces: Modified Multiple Paired Pixel Consistency

Sheng Xiang, Shun’ichi Kaneko, Dong Liang

2020

Abstract

When attempting to examine three-dimensional micro-textured surfaces or illumination fluctuations, problems such as shadowing can occur with many conventional visual inspection methods. Thus, we propose a modified method comprising orientation codes based on consistency of multiple pixel pairs to inspect defects in logotypes printed on three-dimensional micro-textured surfaces. This algorithm comprises a training stage and a detection stage. The aim of the training stage is to locate and pair supporting pixels that show similar change trends as a target pixel and create a statistical model for each pixel pair. Here, we introduce our modified method that uses the chi-square test and skewness to increase the precision of the statistical model. The detection stage identifies whether the target pixel matches its model and judges whether it is defective or not. The results show the effectiveness of our proposed method for detecting defects in real product images.

Paper Citation

in Harvard Style

Xiang S., Kaneko S. and Liang D. (2020). Robust Method for Detecting Defect in Images Printed on 3D Micro-textured Surfaces: Modified Multiple Paired Pixel Consistency. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 260-267. DOI: 10.5220/0008910002600267

in Bibtex Style

@conference{visapp20,
author={Sheng Xiang and Shun’ichi Kaneko and Dong Liang},
title={Robust Method for Detecting Defect in Images Printed on 3D Micro-textured Surfaces: Modified Multiple Paired Pixel Consistency},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={260-267},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008910002600267},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Robust Method for Detecting Defect in Images Printed on 3D Micro-textured Surfaces: Modified Multiple Paired Pixel Consistency
SN - 978-989-758-402-2
AU - Xiang S.
AU - Kaneko S.
AU - Liang D.
PY - 2020
SP - 260
EP - 267
DO - 10.5220/0008910002600267
PB - SciTePress