Automatic recognition of weave pattern and repeat for yarn-dyed fabric based on KFCM and IDMF

Abstract This paper proposes an automatic recognition method to analyze the weave pattern and repeat of yarn-dyed fabrics. Firstly, the warp and weft floats of preprocessing yarn-dyed fabric images with the solid color are segmented through gray projection method. The kernel fuzzy c-means clustering (KFCM) algorithm is utilized to classify the weave points based on the texture features of gray means, gray variances and gray level co-occurrence matrix (GLCM). The exact state of the two floats is judged by comparing average gray means of each cluster. With warp floats (1s) and weft floats (0s), fabric image is represented as binary value weave diagram and coded digital matrix. Then, improved distance matching function (IDMF) is employed to obtain the weave repeat of weave diagram, which is used to correct error floats and improve the accuracy of identification result. Moreover, IDMF is directly applied to yarn-dyed fabrics with different color yarns and obtained the accurate weave repeat with faster speed. The experimental results have shown that the proposed algorithm can recognize weave pattern and repeat accurately and faster, and output the corresponding binary value weave diagram of the identified fabric.

[1]  Chung-Feng Jeffrey Kuo,et al.  Application of computer vision in the automatic identification and classification of woven fabric weave patterns , 2010 .

[2]  Maziar Palhang,et al.  A novel method for the identification of weave repeat through image processing , 2009 .

[3]  Dejun Zheng,et al.  A new method for classification of woven structure for yarn-dyed fabric , 2014 .

[4]  Jihong Liu,et al.  Automatic recognition of woven fabric pattern based on image processing and BP neural network , 2011 .

[5]  Sung Yong Shin,et al.  Fast determination of textural periodicity using distance matching function , 1999, Pattern Recognit. Lett..

[6]  Shi Yu-feng,et al.  Remote sensing image classification and recognition based on KFCM , 2010, 2010 5th International Conference on Computer Science & Education.

[7]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[8]  George Baciu,et al.  Investigation on the Classification of Weave Pattern Based on an Active Grid Model , 2009 .

[9]  Xin Wang,et al.  Fabric Texture Analysis Using Computer Vision Techniques , 2011, IEEE Transactions on Instrumentation and Measurement.

[10]  Yang Li,et al.  Automatic Classification of Woven Fabric Structure by Using Learning Vector Quantization , 2011 .

[11]  Wan-Jui Lee,et al.  A Kernel-Based Fuzzy Clustering Algorithm , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[12]  Chung-Yang Shih,et al.  Automatic Recognition of Fabric Weave Patterns by a Fuzzy C-Means Clustering Method , 2004 .

[13]  P. Nagabhushan,et al.  Automatic extraction of texture-periodicity using superposition of distance matching functions and their forward differences , 2012, Pattern Recognit. Lett..

[14]  Dejun Zheng,et al.  Entropy-Based Fabric Weave Pattern Indexing and Classification , 2010, Int. J. Cogn. Informatics Nat. Intell..

[15]  Jihong Liu,et al.  Automatic Detection of Structure Parameters of Yarn-dyed Fabric , 2010 .