Fast and Parallel Summed Area Table for Fabric Defect Detection

Automating fabric defect detection has a significant role in fabric industries. However, the existing fabric defect detection algorithms lack the real-time performance that is required in real applications due to their high demanding computation. To ensure real time, high accuracy and reliable fabric defect detection this paper developed a fast and parallel normalized cross-correlation algorithm based on summed-area table technique called PFDD-SAT. To meet real-time requirements, extensive use of the NVIDIA CUDA framework for Graphical Processing Unit (GPU) computing is made. The detailed implementation steps of the PFDD-SAT are illustrated in this paper. Several experiments have been carried out to evaluate the detection time and accuracy and then the robustness to illumination and Gaussian noises. The results show that the PFDD-SAT has robustness to noise and speeds the defect detection process more than 200 times than normal required time and that greatly met the needs for real-time automatic fabric defect detection.

[1]  Du-Ming Tsai,et al.  Wavelet-based defect detection in solar wafer images with inhomogeneous texture , 2012, Pattern Recognit..

[2]  Olga Veksler,et al.  Fast variable window for stereo correspondence using integral images , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  Anders Eklund,et al.  Medical image processing on the GPU - Past, present and future , 2013, Medical Image Anal..

[4]  Du-Ming Tsai,et al.  Rotation-invariant pattern matching with color ring-projection , 2002, Pattern Recognit..

[5]  Jian Zhou,et al.  Fast and efficient fully 3D PET image reconstruction using sparse system matrix factorization with GPU acceleration , 2011, Physics in medicine and biology.

[6]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[7]  Imen Chakroun,et al.  Combining multi-core and GPU computing for solving combinatorial optimization problems , 2013, J. Parallel Distributed Comput..

[8]  Kenli Li,et al.  Scheduling Precedence Constrained Stochastic Tasks on Heterogeneous Cluster Systems , 2015, IEEE Transactions on Computers.

[9]  Kenli Li,et al.  Performance Optimization Using Partitioned SpMV on GPUs and Multicore CPUs , 2015, IEEE Transactions on Computers.

[10]  Pradeep M. Patil,et al.  A review of automatic fabric defect detection techniques , 2009 .

[11]  Kenli Li,et al.  Performance Analysis and Optimization for SpMV on GPU Using Probabilistic Modeling , 2015, IEEE Transactions on Parallel and Distributed Systems.

[12]  Jun Feng,et al.  Study of fabric defects detection through Gabor filter based on scale transformation , 2010, 2010 International Conference on Image Analysis and Signal Processing.

[13]  Grantham Pang,et al.  Discriminative training approaches to fabric defect classification based on wavelet transform , 2004, Pattern Recognit..

[14]  Qiuping Zhu,et al.  Fabric defect detection via small scale over-complete basis set , 2014 .

[15]  Shekhar R. Suralkar,et al.  Overview: Methods of Automatic Fabric Defect Detection , 2012 .

[16]  Mohd Awais Farooque,et al.  SURVEY ON VARIOUS NOISES AND TECHNIQUES FOR DENOISING THE COLOR IMAGE , 2013 .

[17]  Hyunsu Cho,et al.  Pattern classification of solder joint images using a correlation neural network , 1996 .

[18]  Laurent Bigue,et al.  Optimization of automated online fabric inspection by fast Fourier transform (FFT) and cross-correlation , 2013 .

[19]  Maria Petrou,et al.  Automatic registration of ceramic tiles for the purpose of fault detection , 2000, Machine Vision and Applications.

[20]  Hans Knutsson,et al.  fMRI analysis on the GPU - Possibilities and challenges , 2012, Comput. Methods Programs Biomed..

[21]  N. H. C. Yung,et al.  Performance Evaluation for Motif-Based Patterned Texture Defect Detection , 2010, IEEE Transactions on Automation Science and Engineering.

[22]  Olivier Alata,et al.  Unsupervised textured image segmentation using 2-D quarter plane autoregressive model with four prediction supports , 2005, Pattern Recognit. Lett..

[23]  Du-Ming Tsai,et al.  Automatic Band Selection for Wavelet Reconstruction in the Application of Defect Detection , 2022 .

[24]  Richard D. Zakia,et al.  The Focal encyclopedia of photography , 1993 .

[25]  Ajay Kumar,et al.  Computer-Vision-Based Fabric Defect Detection: A Survey , 2008, IEEE Transactions on Industrial Electronics.

[26]  Kenli Li,et al.  A cost-optimal parallel algorithm for the 0-1 knapsack problem and its performance on multicore CPU and GPU implementations , 2015, Parallel Comput..

[27]  Junfeng Jing,et al.  Defect Detection on Printed Fabrics Via Gabor Filter and Regular Band , 2015 .

[28]  Kenli Li,et al.  Energy-Efficient Stochastic Task Scheduling on Heterogeneous Computing Systems , 2014, IEEE Transactions on Parallel and Distributed Systems.

[29]  Dmitry Chetverikov.  Structural defects: general approach and application to textile inspection , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[30]  Michael K. Ng,et al.  Wavelet based methods on patterned fabric defect detection , 2005, Pattern Recognit..

[31]  Ajay Kumar,et al.  Neural network based detection of local textile defects , 2003, Pattern Recognit..

[32]  Y. M. Fouda Khaled Ragab and,et al.  Parallel Vectoring Algorithm for Pattern Matching , 2014 .

[33]  Kenli Li,et al.  Accelerating solidification process simulation for large-sized system of liquid metal atoms using GPU with CUDA , 2014, J. Comput. Phys..

[34]  K. Srinivasan,et al.  FDAS: A Knowledge-based Framework for Analysis of Defects in Woven Textile Structures , 1992 .

[35]  Lin Shi,et al.  A survey of GPU-based medical image computing techniques. , 2012, Quantitative imaging in medicine and surgery.

[36]  J. P. Lewis.  Fast Normalized Cross-Correlation , 2010 .

[37]  Errol J. Wood,et al.  Applying Fourier and Associated Transforms to Pattern Characterization in Textiles , 1990 .

[38]  Uwe D. Hanebeck,et al.  Template matching using fast normalized cross correlation , 2001, SPIE Defense + Commercial Sensing.

[39]  Paolo Valigi,et al.  Automated defect detection in uniform and structured fabrics using Gabor filters and PCA , 2013, J. Vis. Commun. Image Represent..

[40]  N. H. C. Yung,et al.  Automated fabric defect detection - A review , 2011, Image Vis. Comput..

[41]  Xianghua Xie,et al.  A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques , 2008 .

[42]  Shih-hsuan Chiu,et al.  Textural Defect Segmentation Using a Fourier-Domain Maximum Likelihood Estimation Method , 2002 .

[43]  Jeffrey S. Vetter,et al.  A Survey of Methods for Analyzing and Improving GPU Energy Efficiency , 2014, ACM Comput. Surv..

[44]  Franklin C. Crow,et al.  Summed-area tables for texture mapping , 1984, SIGGRAPH.

[45]  Georgios D. Evangelidis,et al.  Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.