Fabric defect detection based on adaptive local binary patterns

Authors:
Rong Fu;Meihong Shi;Hongli Wei;Huijuan Chen
Affiliations:
School of Computer Science, Xi'an Polytechnic University and School of Electronic Engineering, Xidian University, Xi'an, China;School of Computer Science, Xi'an Polytechnic University, Xi'an, China;School of Electronic Information Engineering, Xi'an Technological University, Xi'an, China;School of Computer Science, Xi'an Polytechnic University
Venue:
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Year:
2009

Citing 6
Cited 0

Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Texture Classification by Subsets of Local Binary Patterns

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Fabric defect detection using modified local binary patterns

EURASIP Journal on Advances in Signal Processing
Fabric Defect Detection Based on Wavelet Decomposition with One Resolution Level

ISISE '08 Proceedings of the 2008 International Symposium on Information Science and Engieering - Volume 01
Defect Detection in Textiles Using Morphological Analysis of Optimal Gabor Wavelet Filter Response

ICCAE '09 Proceedings of the 2009 International Conference on Computer and Automation Engineering
Dominant local binary patterns for texture classification

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Adaptive local binary patterns method is proposed in this paper, on which an effective fabric defect detection algorithm is designed. ALBP method selects the frequently occurred patterns to construct the main pattern set, which avoids using the same pattern set to describe different texture structures in uniform local binary patterns method. The features of free defect image are extracted according to the set and the threshold is confirmed. The image to be tested is divided into same size detection windows from which ALBP features are also extracted. Defective window is found through comparing ALBP features with threshold. The experiment exhibited the detection effect of the proposed method is comparatively better than traditional LBP method from human visual aspect and detection accuracy.