Computer Science ›› 2018, Vol. 45 ›› Issue (11): 267-271.doi: 10.11896/j.issn.1002-137X.2018.11.042
• Graphics, Image & Pattern Recognition • Previous Articles Next Articles
ZHANG Sai1, RUI Ting1,2, REN Tong-wei2, YANG Cheng-song1, ZOU Jun-hua1
CLC Number:
[1]ZHANG H Y,PENG Q C.A survey on digital image inpainting[J].Journal of Image & Graphics,2007,12(1):1-10.(in Chinese) 张红英,彭启琮.数字图像修复技术综述[J].中国图象图形学报,2007,12(1):1-10. [2]BERTALMÍO M,SAPIRO G,CASELLES V,et al.Image in- painting[C]∥Conference on Computer Graphics and Interactive Techniques.DBLP,2000:417-424. [3]CHAN T,SHEN J.Mathematical models for local non-texture inpaintings[J].SIAM Journal on Applied Mathematics,2001,62(3):1019-1043. [4]KOMODAKIS N.Image Completion Using Global Optimization[C]∥2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.IEEE,2006:442-452. [5]HSIN H F,LEOU J J,LIN C S,et al.Image inpaintingusingstructure-guided priority belief propagation and label transformations[C]∥International Conference on Pattern Recognition.IEEE,2010:4492-4495. [6]MANSFIELD A,PRASAD M,ROTHER C,et al.Transforming Image Completion[C]∥British Machine Vision Conference.2011. [7]KANG Y,LEE K T,EUN J,et al.Stacked Denoising Autoencoders for Face Pose Normalization[C]∥International Con-ference on Neural Information Processing.Springer Berlin Heidelberg,2013:241-248. [8]XIE J,XU L,CHEN E.Image denoising and inpainting with deep neural networks[C]∥International Conference on Neural Information Processing Systems.2012:341-349. [9]PATHAK D,KRAHENBUHL P,DONAHUE J,et al.Context Encoders:Feature learning by inpainting[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2016:2536-2544. [10]YANG C,LU X,LIN Z,et al.High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis[C]∥IEEECon-ference on Computer Vision and Pattern Recognition.IEEE,2017:4076-4084. [11]HUANG R,CHANG L,LIC G,et al.Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition[J].Mathematical Problems in Engineering,2016,2016(5):1-14. [12]HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527. [13]BENGIO Y.Learning deep architectures for AI[J].Foundations and Trends in Machine Learning,2009,2(1):1-127. [14]ZHANG C,ZHANG Z Y.Improving multiview face detection with multi-task deep convolutional neural networks[C]∥IEEE Winter Application and Computer Vision Conference.USA,2014:1036-1041. [15]LÄNGKVIST M,KARLSSON L,LOUTFI A.A review of unsupervised feature learning and deep learning for time-series modeling[J].Pattern Recognition Letters,2014,42(1):11-24. [16]ZHANG Y,CHEN Q Y,ZHANG Y J.Deep learning and its new progress in object and behavior recognition[J].Journal of Image & Graphics,2014,19(2):175-184.(in Chinese) 郑胤,陈权崎,章毓晋.深度学习及其在目标和行为识别中的新进展[J].中国图象图形学报,2014,19(2):175-184. [17]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[J].Nature,1986,323:533-536. [18]PEARSON K.Mathematical contributions to the theory of evolution (III):Regression,heredity,and panmixia[J].Prceedings of the Royal Society of London,1998,187(4):253-318. |
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