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[1]林碧珺,耿增民,洪颖,等.卷积神经网络在纺织及服装图像领域的应用[J].北京服装学院学报(自然科学版),2021,(1):92-99.
 LIN Bi-jun,GENG Zeng-min,HONG Ying,et al.Application of Convolutional Neural Network in Textile andClothing Image Field[J].Journal of Beijing Institute of fashion Technology,2021,(1):92-99.
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卷积神经网络在纺织及服装图像领域的应用

参考文献/References:

[1] DAVID A F, JEAN P. Computer vision: A modern approach[M]. 2nd edition. Boston: Pearson Education, 2012:3 -5. [2] 卢宏涛, 张秦川. 深度卷积神经网络在计算机视觉中的应用研 究综述[J]. 数据采集与处理, 2016(1):1 -17. LU H T, ZHANG Q CH. A review of the application of deep convo- lutional neural network in computer vision [J]. Data Acquisition and Processing, 2016 (1): 1 -17. [3] HUBEL D H, WIESEL T N. Receptive fields, binocular interaction and functional architecture in the cat蒺s visual cortex[J]. The Jour- nal of Physiology, 1962, 160(1): 106 -154. [4] FUKUSHIMA K, MIYAKE S. Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position [J]. Pattern Recognition, 1982, 15(6):455 -469. [5] LECUN Y, BOTTOU L. Gradient-based learning applied to docu- ment recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278 -2324. [6] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553):436. [7] GU J, WANG Z, KUEN J, et al. Recent advances in convolutional neural networks[J]. Computer Science, 2016(6):1 -14. [8] BOUREAU Y L, ROUX N I, BACH F, et al. Ask the locals: Multi-way local pooling for image recognition [A]. Proceedings of the 2011 International Conference on Computer Vision [C]. Barce- lona, Spain, 2011: 2651 -2658 [9] ZEILER M D, FERGUS R. Stochastic pooling for regularization of deep convolutional neural networks[J]. Eprint Arxiv, 2013(1): 1 -9. [10] SAINATH T N, MOHAMED A R, KINGSBURY B, et al. Deep convolutional neural networks for LVCSR[C]椅Acoustics, Speech and Signal Processing (ICASSP). 2013 IEEE International Confer- ence on. IEEE, 2013. [11] LECUN Y, BOSER B, DENKER J, et al. Backpropagation ap- plied to handwritten zip code recognition[J]. Neural Computa- tion, 1989, 1(4):541 -551. [12] LECUN Y, BOTTOU L. Gradient-based learning applied to docu- ment recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278 -2324. [13] KRIZHEVSKY A, SUTSKEVER I, HINTON G. Imagenet classifi- cation with deep convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2012, 25(2):1 -9. [14] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolu- tions[C]椅2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. [15] SIMONYAN K, ZISSERMAN A. Very deep convolutional net- works for large-scale image recognition[J]. Computer Science, 2015(4):1 -14. [16] HE K, ZHANG X, REN S, et al. Deep residual learning for im- age recognition[C]椅IEEE Conference on Computer Vision & Pat- tern Recognition. IEEE Computer Society, 2016. [17] DENG J, DONG W, SOCHER R, et al. Imagenet: A large-scale hierarchical image database[C] 椅2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, Florida, USA. IEEE, 2009. [18] FERGUS R, PERONA P. Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories[J]. Computer Vision & Image Under- standing, 2007, 106(1):59 -70. [19] TORRALBA A, FERGUS R, FREEMAN W T. 80 Million Tiny Images: A large data set for nonparametric object and scene recog- nition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11):1958 -1970. [20] XIAO J, HAYS J, EHINGER K A, et al. SUN database: Large- scale scene recognition from abbey to zoo[C]椅The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13 - 18 June 2010. IEEE, 2010. [21] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]椅CVPR. IEEE, 2014. [22] HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transac- tions on Pattern Analysis & Machine Intelligence, 2014, 37(9): 1904 -16. [23] GIRSHICK R. Fast R-CNN[C]椅2015 IEEE International Con- ference on Computer Vision (ICCV). IEEE, 2016. [24] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards re- al-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(6):1137 -1149. [25] Uijlings J R R , K. E. A. van de Sande…. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2):154 -171. [26] LIU Z, LUO P, QIU S, et al. Deep Fashion: Powering robust clothes recognition and retrieval with rich annotations[C]椅2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. [27] KUO C F J, LEE C L, SHIH C Y. Image database of printed fab- ric with repeating dot patterns part (I)-image archiving[J]. Tex- tile Research Journal, 2016:004051751666316. [28] 路凯. 羊绒羊毛纤维显微视觉特征表达与识别算法研究[D]. 上海:东华大学,2018. LU K. Research on micro vision feature expression and recognition algorithm of cashmere and wool fiber [ D]. Shanghai:Donghua University, 2018. [29] 王飞, 靳向煜. 应用卷积网络及深度学习理论的羊绒与羊毛鉴别[J]. 纺织学报, 2017(12):156 -162. WANG F, JIN X Y. Identification of cashmere and wool using convolution network and deep learning theory [J]. Acta Textile Sinica, 2017 (12): 156 -162. [30] 何晓昀, 韦平, 张林,等. 基于深度学习的籽棉中异性纤维检 测方法[J]. 纺织学报, 2018, 39(6):131 -135. HE X Y, WEI P, ZHANG L, et al. Detection method of foreign fiber in seed cotton based on deep learning [J]. Acta textile Sini- ca, 2018, 39(6): 131 -135. [31] WICAKSONO A Y, SUCIATI N, FATICHAH C, et al. Modified convolutional neural network architecture for batik motif image classification[J]. Research Gate, 2017. [32] 贾小军, 叶利华, 邓洪涛, 等. 基于卷积神经网络的蓝印花布 纹样基元分类[J]. 纺织学报, 2020, 41(1):110 -117. JIA X J, YE L H, DENG H T, et al. Pattern primitive classifica- tion of blue calico based on convolutional neural network [J]. Ac- ta Textile Sinica, 2020, 41(1): 110 -117. [33] 张宏伟,张凌婕,李鹏飞. 基于深度卷积神经网络的织物花型 分类[J]. 纺织高校基础科学学报,2017,30(2):261 - 265, 271. ZHANG H W, ZHANG L J, LI P F. Fabric pattern classification based on deep convolution neural network [J]. Journal of Basic Science of Textile University, 2017,30(2): 261 -265,271. [34] 张玮,张华熊. 基于卷积神经网络的纺织面料主成分分类[J]. 浙江理工大学学报(自然科学版),2019,41(1):1 -8. ZHANG W, ZHANG H X. Principal component classification of textile fabrics based on convolutional neural network [J]. Journal of Zhejiang University of Science and Technology (Natural Science Edition), 2019,41(1): 1 -8. [35] 晏琳,景军锋,李鹏飞. Faster RCNN 模型在坯布疵点检测中的 应用[J]. 棉纺织技术,2019,47(2):24 -27. YAN L, JING J F, LI P F. Application of fast RCNN model in grey fabric defect detection [ J]. Cotton Textile Technology, 2019,47(2): 24 -27. [36] 史甜甜. 基于Fisher 准则的深层卷积神经网络织物疵点检测 [J]. 计算机系统应用, 2019, 28(3):142 -147. SHI T T. Fabric defect detection based on deep convolution neural network based on Fisher criterion [J]. Computer System Applica- tion, 2019, 28(3): 142 -147. [37] 王萌萌,刘成霞. 基于卷积神经网络的织物缝纫平整度客观评 价[J]. 毛纺科技,2020,48(5):87 -91. WANG M M, LIU C X. Objective evaluation of fabric sewing smoothness based on convolution neural network [J]. Wool Textile Technology, 2020,48(5): 87 -91. [38] KRIZHEVSKY A, HINTON G. Learning multiple layers of fea- tures from tiny images [D]. Toronto: University of Toronto,2009: 3 -21. [39] HOWARD A G , ZHU M , CHEN B , et al. Mobile Nets: Effi- cient convolutional neural networks for mobile vision applications [J]. Research Gate, 2017:1 -9. [40] NAWAZ M M T, HASAN R, HASAN M A, et al. Automatic cat- egorization of traditional clothing using convolutional neural net- work[C] 椅IEEE/ ACIS International Conference on Computer & Information Science. IEEE, 2018. [41] LAO B, JAGADEESH K. Convolutional neural networks for fash- ion classification and object detection [EB/ OL]. [2015 - 09 - 07]. [42] DONG CH Y, SHI Y Q,TAO R, et al. Convolutional Neural Net- works for Clothing Image Style Recognition[C]椅2018. [43] 胡聪, 屈瑾瑾, 许川佩,等. 基于自适应池化的神经网络的服 装图像识别[J]. 计算机应用, 2018, 38(8):2211 -2217. HU C, QU J J, XU C P, et al. Clothing image recognition based on adaptive pooling neural network [J]. Computer Applications, 2018, 38 (8): 2211 -2217. [44] 刘正东, 刘以涵, 王首人. 西装识别的深度学习方法[J]. 纺 织学报, 2019, 40(4):158 -164. LIU Z D, LIU Y H, WANG S R. Deep learning method of suit recognition [J]. Acta Textile Sinica, 2019, 40(4): 158 -164. [45] 陈双,何利力. 基于Faster R-CNN 的服装目标检测改进方法 [J]. 软件导刊,2020,19(4):1 -5. CHEN S, HE L L. Improved method of clothing target detection based on fast R-CNN[J]. Software Gguide, 2020,19(4):1 -5. [46] CYCHNERSKI J, BRZESKI A, BOGUSZEWSKI A, et al. Clothes detection and classification using convolutional neural net- works[C]椅IEEE International Conference on Emerging Technolo- gies & Factory Automation. IEEE, 2017. [47] IANDOLA F N, HAN S, MOSKEWICZ M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0郾5 MB model size[ Z]. Under Review as a Conference Paper at ICLR 2017. [48] HARA K, JAGADEESH V, PIRAMUTHU R. Fashion apparel de- tection: The role of deep convolutional neural network and pose dependent priors[C/ OL]. [2015 -09 -20]. [49] 陈丽丽, 韩润萍. 基于卷积神经网络的服饰图像分类[J]. 北 京服装学院学报(自然科学版), 2018, 38(4):35 -40,62. CHEN L L, HAN R P. Classification of clothing images based on convolutional neural network [J]. Journal of Beijing Institute of Clothing Technology (Natural Science Edition), 2018, 38(4): 35 -40,62. [50] SHANNA W, HUAXIONG Z, FENG K. Emotion classification of necktie pattern based on convolution neural network[J]. Journal of Textile Research, 2018.

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备注/Memo

收稿日期: 2020 -09 -23 基金项目: 北京教委科技计划一般项目(KM202010012008) 作者简介: 林碧珺(1997—),女,硕士研究生。 通信作者: 耿增民,教授,主要从事服装设计与工程等研究;E-mail:jsjgzm@ bift. edu. cn。

更新日期/Last Update: 1900-01-01