{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:15:17Z","timestamp":1760242517562,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,10,13]],"date-time":"2017-10-13T00:00:00Z","timestamp":1507852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance) and stability (selectivity) of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture) and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep) extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation.<\/jats:p>","DOI":"10.3390\/a10040117","type":"journal-article","created":{"date-parts":[[2017,10,13]],"date-time":"2017-10-13T11:34:09Z","timestamp":1507894449000},"page":"117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network"],"prefix":"10.3390","volume":"10","author":[{"given":"Babar","family":"Khan","sequence":"first","affiliation":[{"name":"Engineering Research Center of Digitized Textile and Apparel Technology, College of Information Science and Technology, Donghua University, Shanghai 201620, China"}]},{"given":"Zhijie","family":"Wang","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Digitized Textile and Apparel Technology, College of Information Science and Technology, Donghua University, Shanghai 201620, China"}]},{"given":"Fang","family":"Han","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Digitized Textile and Apparel Technology, College of Information Science and Technology, Donghua University, Shanghai 201620, China"}]},{"given":"Ather","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Digitized Textile and Apparel Technology, College of Information Science and Technology, Donghua University, Shanghai 201620, China"}]},{"given":"Rana","family":"Masood","sequence":"additional","affiliation":[{"name":"College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1177\/004051759606600803","article-title":"Identifying Fabric Structures with Fast Fourier Transform Techniques","volume":"66","author":"Xu","year":"1996","journal-title":"Text. Res. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1177\/004051759506501108","article-title":"Fourier Transform Analysis of Plain Weave Fabric Appearance","volume":"65","author":"Ravandi","year":"1995","journal-title":"Text. Res. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1177\/004051750007000603","article-title":"Woven Fabric Analysis by Image Processing Part I: Identification of Weave Patterns","volume":"70","author":"Huang","year":"2000","journal-title":"Text. Res. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1177\/004051759906900201","article-title":"Automatic Recognition of Fabric Weave Patterns by Digital Image Analysis","volume":"69","author":"Kang","year":"1999","journal-title":"Text. Res. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1177\/004051750407400204","article-title":"Automatic Recognition of Fabric Weave Patterns by Fuzzy C-Means Clustering Method","volume":"74","author":"Kuo","year":"2004","journal-title":"Text. Res. J."},{"key":"ref_7","first-page":"61","article-title":"A New Statistical Approach for Texture Analysis","volume":"56","author":"Wang","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1177\/0040517508101459","article-title":"Investigation on the Classification of Weave Pattern based on an Active Grid Model","volume":"79","author":"Xin","year":"2009","journal-title":"Text. Res. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1016\/j.patrec.2009.05.010","article-title":"Palmprint Verification Using Binary Orientation Co-Occurrence Vector","volume":"30","author":"Guo","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1108\/09556221011018577","article-title":"Recognition and Revisualization of Woven Fabric Structures","volume":"22","author":"Potiyaraj","year":"2010","journal-title":"Int. J. Cloth. Sci. Tech."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.ultras.2009.09.002","article-title":"Influence of Temperature Variations on the Entropy and Correlation of the Grey-Level Co-Occurrence Matrix from B-Mode Images","volume":"50","author":"Alvarenga","year":"2010","journal-title":"Ultrasonics"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hu, Y., Zhao, C.X., and Wang, H.N. (2008, January 19\u201320). Directional Analysis of Texture Images Using Gray Level Co-Occurrence Matrix. Proceedings of the IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Wuhan, China.","DOI":"10.1109\/PACIIA.2008.279"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xie, Z., Gao, J., and Wu, K. (2010, January 12\u201315). Beyond Shape: Incorporating Color Invariance into a Biologically Inspired Feed-Forward Model of Category Recognition. Proceedings of the 7th Indian Conference on Computer Vision, Graphics and Image Processing, Chennai, India.","DOI":"10.1145\/1924559.1924571"},{"key":"ref_14","unstructured":"Jalali, S., Tan, C., Lim, J., Tham, J., Ong, S., Seekings, P., and Taylor, E. (August, January 29). Visual Recognition Using a Combination of Shape and Color Features. Proceedings of the Annual Conference of the Cognitive Science Society, Berlin, Germany."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/S1672-6529(14)60040-8","article-title":"Modulating a Local Shape Descriptor through Biologically Inspired Color Feature","volume":"2","author":"Zhao","year":"2014","journal-title":"J. Bionic Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14955","DOI":"10.1523\/JNEUROSCI.4348-10.2010","article-title":"Advances in color science: From retina to behavior","volume":"30","author":"Conway","year":"2010","journal-title":"J. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1108\/AA-11-2015-100","article-title":"Bio-Inspired Approach to Invariant Recognition and Classification of Fabric Weave Patterns and Yarn Color","volume":"36","author":"Khan","year":"2016","journal-title":"Assem. Autom."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2014.2304638","article-title":"The SpiNNaker Project","volume":"102","author":"Furber","year":"2014","journal-title":"Proc. IEEE"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/JPROC.2014.2313565","article-title":"Neurogrid: A mixed Analog\u2013Digital Multichip System for Large-Scale Neural Simulations","volume":"102","author":"Benjamin","year":"2014","journal-title":"Proc. IEEE"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neucom.2015.03.110","article-title":"Deep extreme learning machines: Supervised autoencoding architecture for classification","volume":"174","author":"Tissera","year":"2016","journal-title":"J. Neurocomput."},{"key":"ref_21","unstructured":"Eliasmith, C., and Anderson, C.H. (2003). Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems, The MIT Press."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","article-title":"Extreme learning machines: A survey","volume":"2","author":"Huang","year":"2011","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MIS.2013.140","article-title":"Extreme learning machines","volume":"28","author":"Cambria","year":"2013","journal-title":"IEEE Intell. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1017\/S0305004100030401","article-title":"A generalized inverse for matrices","volume":"51","author":"Penrose","year":"1955","journal-title":"Math. Proc. Camb. Philos. Soc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1007\/s12559-014-9255-2","article-title":"An insight into extreme learning machines: random neurons, random features and kernels","volume":"6","author":"Huang","year":"2014","journal-title":"Cognit. Comput."},{"key":"ref_27","first-page":"31","article-title":"Representational learning with ELMs for big data","volume":"28","author":"Kasun","year":"2013","journal-title":"IEEE Intell. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.neucom.2014.03.077","article-title":"Learning deep representations via extreme learning machines","volume":"149","author":"Yu","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.neucom.2013.01.057","article-title":"Hierarchical extreme learning machine for feedforward neural network","volume":"128","author":"Han","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.neucom.2012.01.042","article-title":"Silicon spiking neurons for hardware implementation of extreme learning machines","volume":"102","author":"Basu","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Galluppi, F., Davies, S., Furber, S., Stewart, T., and Eliasmith, C. (2012, January 10\u201315). Real time on-chip implementation of dynamical systems with spiking neurons. Proceedings of the International Joint Conference on Neural Networks IJCNN, Brisbane, Australia.","DOI":"10.1109\/IJCNN.2012.6252706"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Choudhary, S., Sloan, S., Fok, S., Neckar, A., Trautmann, E., Gao, P., Stewart, T., Eliasmith, C., and Boahen, K. (2012, January 11\u201314). Silicon neurons that compute. Proceedings of the International Conference on Artificial Neural Networks and Machine Learning (ICANN 2012), Lausanne, Switzerland.","DOI":"10.1007\/978-3-642-33269-2_16"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neunet.2013.02.008","article-title":"Learning the pseudo inverse solution to network weights","volume":"45","author":"Tapson","year":"2013","journal-title":"Neural Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning deep architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"McDonnell, M.D., Tissera, M.D., Ladusich, T.V., van Schaik, A., and Tapson, J. (2015). Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm. PLOS ONE, 10.","DOI":"10.1371\/journal.pone.0134254"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhu, W., Miao, J., and Qing, L. (2014, January 6\u201311). Constrained extreme learning machine: A novel highly discriminative random feedforward neural network. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2014), Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889761"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1109\/TPAMI.2007.56","article-title":"Robust object recognition with cortex-like mechanism","volume":"29","author":"Serre","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Van de Weijer, J., and Schmid, C. (2006, January 7\u201313). Coloring local feature extraction. Proceedings of the 9th European Conference on Computer Vision\u2013Volume Part II (ECCV\u201906), Graz, Austria.","DOI":"10.1007\/11744047_26"},{"key":"ref_41","unstructured":"Nilsback, M.E., and Zisserman, A. (2006, January 17\u201322). A visual vocabulary for flower classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2006), New York, NY, USA."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classification Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1038\/14819","article-title":"Hierarchical models of object recognition in cortex","volume":"2","author":"Riesenhuber","year":"1999","journal-title":"Nat. Neurosci."},{"key":"ref_44","unstructured":"Serre, T., Wolf, L., and Poggio, T. (2005, January 20\u201325). Object recognition with features inspired by visual cortex. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11263-007-0118-0","article-title":"Object class recognition and localization using sparse features with limited receptive fields","volume":"80","author":"Mutch","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.1109\/TSMCB.2011.2158418","article-title":"Enhanced biologically inspired model for object recognition","volume":"41","author":"Huang","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1109\/TIP.2012.2222900","article-title":"Extended coding and pooling in the HMAX model","volume":"22","author":"Theriault","year":"2013","journal-title":"IEEE Trans. Image Process."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/10\/4\/117\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:47:15Z","timestamp":1760208435000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/10\/4\/117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,13]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["a10040117"],"URL":"https:\/\/doi.org\/10.3390\/a10040117","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2017,10,13]]}}}