Automated Inspection of Textile Fabrics Using Textural Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Verifying edges for visual inspection purposes
Non-Linear Analysis
Defect detection in textured surfaces using color ring-projection correlation
Machine Vision and Applications
Texture exemplars for defect detection on random textures
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Random Texture Defect Detection Using 1-D Hidden Markov Models Based on Local Binary Patterns
IEICE - Transactions on Information and Systems
Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach
Computers and Electronics in Agriculture
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In this paper we present a new approach for the detection of defects in random colour textures. This approach is based on the use of the T2 statistic and it is derived from the MIA strategy (Multivariate Image Analysis) developed in recent years in the field of applied statistics. PCA analysis is used to extract a reference eigenspace from a matrix built by unfolding the RGB raw data of defect-free images. The unfolding is performed compiling colour and spatial information of pixels. New testing images are also unfolded and projected onto the reference eigenspace obtaining a score matrix used to compute the T2 images. These images are converted into defect maps which allow the location of defective pixels. Only very few samples are needed to perform unsupervised training. With regard to literature, the method uses one of the simplest approaches providing low computational costs.