An Inventive Method to Fabric Part Structural Defect Detection Using Frame Harmonizing

  • Dhivya M Department of Computer Science, PSGR Krishnammal College for Women, Coimbatore, Tamilnadu
  • Suganthi D Department of Computer Science, PSGR Krishnammal College for Women, Coimbatore, Tamilnadu
Keywords: Fabric fault, Defect detection, Classification, Edge detection

Abstract

Using a frame harmonizing based approach, this paper examines paper defects. In the textiles industry, the quick cutting and sewing of fabric has resulted in a lot of small mistakes, making this task extremely difficult. Especially these deformities won't be quickly recognized by specialists as well as programming. A novel frame harmonizing method is used in our system to find flaws in the fabric production process. Transformation and filtering techniques are used for the inputted fabric image frame. The conventional outline extraction method Berkeley edge detector is used to extract the edge map. Contour-based features are extracted and classified by K-Nearest Neighbour (KNN) classifier. The experimentation with real-time data set produced the outstanding performance results when compared with state of the art methods.

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References

Damage Detection in Composite Materials Using Lamb Wave Methods ,Seth S. Kessler and S. Mark Spearing Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Bodnarova, A., M. Bennamoun, and K. K. Kubik, Defect detection in textile materials based on aspects of the HVS, In Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on, 5 (1998) 4423-4428. https://doi.org/10.1109/ICSMC.1998.727546

Automatic Structure Analysis and Objective Evaluation of Woven Fabric Using Image Analysis TAE JIN KANG, So0 HYUN CHOI, AND SUNG MIN KIM Department of Fiber arid Polytner Science, Seoiil National University, Seoril, Soiith Korea KYUNG WHA OH Department of Honte Economics Education, Cliung-Ang University, Seoiil, Soiith Korea

M. Shi, R. Fu, Y. Guo, S. Bai, & B. Xu, Fabric defect detection using local contrast deviations. Multimedia Tools and Applications, 52(1) (2011) 147

Mak, Kai-Ling, P. Peng, and K. F. C. Yiu, Fabric defect detection using morphological filters, Image and Vision Computing 27(10) (2009) 1585-1592. https://doi.org/10.1016/j.imavis.2009.03.007

Cawley, Peter, and R. D. Adams, The location of defects in structures from measurements of natural frequencies,The Journal of Strain Analysis for Engineering Design 14(2) (1979) 49-57. https://doi.org/10.1243/03093247V142049

N. Tandon, and A. Choudhury, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribology international 32(8) (1999) 469-480. https://doi.org/10.1016/S0301-679X(99)00077-8

P. M. Mahajan, S. R. Kolhe, and P. M. Patil, A review of automatic fabric defect detection techniques, Advances in Computational Research 1(2) (2009) 18-29.

Kumar, Ajay, and Grantham KH Pang, Defect detection in textured materials using Gabor filters, IEEE Transactions on Industry Applications 38(2) (2002) 425-440. https://doi.org/10.1109/28.993164

Chan, Chi-ho, and Grantham Pang, Fabric defect detection by Fourier analysis." In Industry Applications Conference, 1999. Thirty-Fourth IAS Annual Meeting. Conference Record of the 1999 IEEE, 3 (1999) 1743-1750. http://dx.doi.org/10.1109/IAS.1999.805975

Tsai, Du-Ming, and Tse-Yun Huang, Automated surface inspection for statistical textures, Image and Vision Computing 21(4) (2003) 307-323. https://doi.org/10.1016/S0262-8856(03)00007-6

Tsai, Du-Ming, and Cheng-Huei Chiang, Automatic band selection for wavelet reconstruction in the application of defect detection, Image and Vision Computing 21(5) (2003) 413-431. https://doi.org/10.1016/S0262-8856(03)00003-9

K.L. Mak, and P. Peng, An automated inspection system for textile fabrics based on Gabor filters, Robotics and Computer-Integrated Manufacturing 24(3) (2008) 359-369. https://doi.org/10.1016/j.rcim.2007.02.019

Cho, Che-Seung, Byeong-Mook Chung, and Moo-Jin Park, Development of real-time vision-based fabric inspection system, IEEE Transactions on Industrial Electronics 52(4) (2005) 1073-1079. https://doi.org/10.1109/TIE.2005.851648

Seth S Kessler, S Mark Spearing and Constantinos Soutis. Damage detection in composite materials using Lamb wave methods, Smart Materials and Structures, 11(2) (2002) 269. https://doi.org/10.1088/0964-1726/11/2/310

Abouelela, Ahmed, Hazem M. Abbas, HeshamEldeeb, Abdelmonem A. Wahdan, and Salwa M. Nassar, Automated vision system for localizing structural defects in textile fabrics, Pattern Recognition Letters 26(10) (2005) 1435-1443. https://doi.org/10.1016/j.patrec.2004.11.016

Zhang, GuiMei, JiYuan Xu, and Jian Xin Liu, A new method for recognition partially occluded curved objects under affine transformation, In Intelligent Systems and Knowledge Engineering (ISKE), 2015 10th International Conference on, pp. 456-461. IEEE, 2015. https://doi.org/10.1109/ISKE.2015.52

He, Kaiming, Jian Sun, and Xiaoou Tang, Guided image filtering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6) (2013) 1397-1409. https://doi.org/10.1109/TPAMI.2012.213

D. R. Martin, C. C. Fowlkes, and J. Malik, Learning to detect natural image boundaries using local brightness, color, and texture cues, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5), (2004) 530–549. https://doi.org/10.1109/TPAMI.2004.1273918

J. Puzicha, Y. Rubner, C. Tomasi, and J. Buhmann, Empirical Evaluation of Dissimilarity Measures for Color and Texture, Proceedings of the Seventh IEEE International Conference on Computer Vision, 2 (1999) 1165-1172. https://doi.org/10.1109/ICCV.1999.790412

Mingqiang Yang, KidiyoKpalma, Joseph Ronsin, A Survey of Shape Feature Extraction Techniques, Peng-Yeng Yin. Pattern Recognition, IN-TECH, pp.43-90, 2008.

F. Mokhtarian and A. K. Mackworth, A theory of multiscale, curvature-based shape representation for planar curves, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(8) (1992) 789-805. https://doi.org/10.1109/34.149591

A. C. Jalba, M. H. F. Wilkinson, and J. B. T. M. Roerdink, Shape representation and recognition through morphological curvature scale spaces, IEEE Transactions on Image Processing, 15(2)(2006) 331-341. https://doi.org/10.1109/TIP.2005.860606

S. Loncaric, A survey of shape analysis techniques, Pattern Recognition, 31(8) (1998) 983 -1001. https://doi.org/10.1016/S0031-2023(97)00122-2

O. Koller, N.C. Camgoz, H. Ney, & R., Bowden, Weakly supervised learning with multi-stream CNN-LSTM-HMMs to discover sequential parallelism in sign language videos, IEEE transactions on pattern analysis and machine intelligence, 42(9) (2019) 2306-2320. https://doi.org/10.1109/TPAMI.2019.2911077

S.Y. Kim, H.G. Han, J.W. Kim, S. Lee & T.W. Kim, A hand gesture recognition sensor using reflected impulses, IEEE Sensors Journal, 17(10) (2017) 2975-2976. https://doi.org/10.1109/JSEN.2017.2679220

Published
2022-12-30
How to Cite
M, D., & D, S. (2022). An Inventive Method to Fabric Part Structural Defect Detection Using Frame Harmonizing. International Journal of Computer Communication and Informatics, 4(2), 26-40. https://doi.org/10.34256/ijcci2223



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