2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

图像异常检测研究现状综述

吕承侃 沈飞 张正涛 张峰

吕承侃, 沈飞, 张正涛, 张峰. 图像异常检测研究现状综述. 自动化学报, 2021, 47(x): 1−27 doi: 10.16383/j.aas.c200956
引用本文: 吕承侃, 沈飞, 张正涛, 张峰. 图像异常检测研究现状综述. 自动化学报, 2021, 47(x): 1−27 doi: 10.16383/j.aas.c200956
Lv Cheng-Kan, Shen Fei, Zhang Zheng-Tao, Zhang Feng. Review of image anomaly detection. Acta Automatica Sinica, 2021, 47(x): 1−27 doi: 10.16383/j.aas.c200956
Citation: Lv Cheng-Kan, Shen Fei, Zhang Zheng-Tao, Zhang Feng. Review of image anomaly detection. Acta Automatica Sinica, 2021, 47(x): 1−27 doi: 10.16383/j.aas.c200956

图像异常检测研究现状综述

doi: 10.16383/j.aas.c200956
基金项目: 本文受中国科学院青年创新促进会(2020139)资助
详细信息
    作者简介:

    吕承侃:中国科学院自动化研究所精密感知与控制研究中心博士研究生. 2017年获得山东大学学士学位. 主要研究方向为神经网络, 计算机视觉与图像处理. E-mail: lvchengkan2017@ia.ac.cn

    沈飞:中国科学院自动化研究所精密感知与控制研究中心副研究员. 2012年获得自动化研究所博士学位. 主要研究方向为视觉检测, 机器人视觉控制与微装配. E-mail: fei.shen@ia.ac.cn

    张正涛:中国科学院自动化研究所精密感知与控制研究中心研究员. 2010年获得自动化研究所博士学位. 主要研究方向为视觉测量, 微装配与自动化. 本文通信作者. E-mail: zhengtao.zhang@ia.ac.cn

    张峰:中国科学院自动化研究所精密感知与控制研究中心副研究员. 2012年获得自动化研究所博士学位. 主要研究方向为机器人控制, 机器人视觉控制与微装配. E-mail: feng.zhang@ia.ac.cn

Review of Image Anomaly Detection

Funds: Supported by Youth Innovation Promotion Association, CAS (2020139)
More Information
    Author Bio:

    LV Cheng-Kan Ph.D. candidate at the Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences. He received his B.S. degree from Shandong University in 2017. His research interests include neural networks, computer vision and image processing

    SHEN Fei Associate professor at the Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from Institute of Automation, Chinese Academy of Sciences in 2012. His research interests include visual inspection, robot vision control and micro-assembly

    ZHANG Zheng-Tao Professor at the Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from Institute of Automation, Chinese Academy of Sciences in 2010. His research interests include visual measurement, micro-assembly and automation. Corresponding author of this paper

    ZHANG Feng Associate professor at the Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from Institute of Automation, Chinese Academy of Sciences in 2012. His research interests include robot control, robot vision control and micro-assembly

  • 摘要: 图像异常检测是计算机视觉领域的一个热门研究课题, 其目标是在不使用真实异常样本的情况下, 利用现有的正常样本构建模型以检测可能出现的各种异常图像, 在工业外观缺陷检测, 医学图像分析, 高光谱图像处理等领域有较高的研究意义和应用价值. 本文首先介绍了异常的定义以及常见的异常类型. 然后, 本文根据在模型构建过程中有无神经网络的参与, 将图像异常检测方法分为基于传统方法和基于深度学习两大类型, 并分别对相应的检测方法的设计思路、优点和局限性进行了综述与分析. 其次, 梳理了图像异常检测任务中面临的主要挑战. 最后, 对该领域未来可能的研究方向进行了展望.
  • 图  1  异常的类型[1]. (a)点异常 (b)上下文异常 (c)集群异常

    Fig.  1  The type of anomaly. (a) Point anomaly (b) Contextual anomaly (c) Collective anomaly

    图  2  图像异常分类图

    Fig.  2  The classification of image anomalies

    图  3  图像异常检测技术的分类图

    Fig.  3  The classification of image anomaly detection methods

    图  4  模板匹配的适用场景[48]

    Fig.  4  Applicable scenes of template matching

    图  5  统计模型的适用场景[27, 51, 52]. (a)山区公路图像(b)钢板图像 (c)声呐图像

    Fig.  5  Applicable scenes of statistical model. (a) Moutain road image (b) Steel image (c) Sonar image

    图  6  基于低秩分解的图像异常检测示意图[57]

    Fig.  6  Illustration of anomaly detection based on low-rank decomposition

    图  7  图像分解的适用场景. (a)纹理图像 (b)高光谱图像

    Fig.  7  Applicable scenes of image decomposition. (a) Texture images (b) Hyperspectral image

    图  8  通过背景频谱消除来进行异常检测[66]. (a)原始图像 (b)幅度谱图像 (c)信息筛选 (d)逆变换后图像

    Fig.  8  Anomaly detection based on subtraction of background spectral. (a) Origianl image (b) Amplitude spectrum (c) Information filtering (d) Image after IFT

    图  9  人工构造周期性示意图. (a)在二维平面中构建周期性 (b)在三维空间中构建周期性

    Fig.  9  Illustration of artificial periodicity. (a) Constructing periodicity in 2D plane (b) Constructing periodicity in 3D space

    图  10  周期性较弱时的检测效果[62].(a)原始图像 (b)检测结果

    Fig.  10  Detection result in weakly periodic scene. (a) Original image (b) Detection reuslt

    图  11  稀疏编码中的字典[71]

    Fig.  11  The dictionary learned in sparse encoding

    图  12  纳米材料图像[71]

    Fig.  12  Image of nanofibres

    图  13  OC-SVM和SVDD的示意图[82]. (a) OC-SVM (b) SVDD

    Fig.  13  Illustration of OC-SVM and SVDD. (a) OC-SVM (b) SVDD

    图  14  Deep SVDD的原理示意图[88]

    Fig.  14  Illustration of Deep SVDD

    图  15  FCDD的原理示意图[96]

    Fig.  15  Illustration of FCDD

    图  16  在旋转目标上的检测效果[96]. (a)原始图像(b)检测结果 (c)真值图.

    Fig.  16  Detection results on targets with rotation. (a) Original images (b) Detection results (c) Ground truths.

    图  17  将单类样本转换成多类样本[101]

    Fig.  17  Transforming one-class samples into multi-class samples

    图  18  不同图像上旋转效果对比[102]

    Fig.  18  Comparison of rotation on different images

    图  19  GAN结构示意图[15]

    Fig.  19  The structure of GAN

    图  20  需要构建负样本的几种方法的示意图. (a)基于随机噪声的方法 (b)基于随机图像的方法 (c)基于GAN的方法 (d)基于梯度上升的方法

    Fig.  20  The graphical illustration of the methods based on creating fake-negative samples. (a) Methods based on random noises (b) Methods based on random images (c) Methods based on GAN (d) Methods based on gradient ascent

    图  21  自编码器的结构[119]

    Fig.  21  The structure of autoencoder

    图  22  异常样本的重构示意图[130]

    Fig.  22  The reconstruction of anomalous images

    图  23  隐变量编辑示意图[132]

    Fig.  23  The editing of latent vector

    图  24  结合自编码器和GAN进行图像重构[107]

    Fig.  24  Image reconstruction based on autoencoder and GAN

    图  25  医学图像的重构[16,142]. (a)原始图像 (b)重构图像

    Fig.  25  Reconstruction of medical images. (a) Original images (b) Reconstructed images

    图  26  工业图像中的微小异常[146]

    Fig.  26  Tiny anomaly in industrial image

    表  1  图像异常检测的应用领域

    Table  1  Applications of image anomaly detection

    应用领域具体应用
    缺陷检测各种产品表面缺陷检测, 包括布匹[8]、玻璃[9]、钢板[10]、水泥[11]等纹理表面以及印制电路板[12]、酒瓶[13]等物体表面缺陷的检测.
    医学影像分析在核磁共振图像[14]、虹膜图像[15]、眼底视网膜图像[16]等医学图像中检测可能的病变区域.
    高光谱图像处理海面船舶检测[17]、地面异常区域检测[18], 机场飞机定位[19]
    下载: 导出CSV

    表  2  基于传统方法的图像异常检测技术的分类和特点

    Table  2  The classification and characteristic of traditional image anomaly detection methods

    方法类别设计思路优点缺点参考文献
    模板匹配建立待测图像和模板图像之间的对应关系, 通过比较得到异常区域方法简单有效, 对于采集环境高度可控的场景有很高的检测精度不适用于多变的场景或目标[29-48]
    统计模型通过统计学方法构建背景模型具有详实的理论基础和推导过程, 检测速度快需要大量的训练样本, 且仅适用于一些简单背景下的异常检测[49-54]
    图像分解将原始图像分解成代表背景的低秩矩阵和代表异常区域的稀疏矩阵具有详实的理论基础且无需训练过程速度较慢, 而且不适合在结构复杂的图像中进行异常检测[56-61]
    频域分析通过编辑图像的频谱信息来消除图像中重复的背景纹理部分以凸显异常区域无需训练过程, 检测速度很快还需更详实的理论论证, 且仅适用于一些有重复性纹理的图像, 通用性较差[64-70]
    稀疏编码重构借助稀疏编码和字典学习等方式学习正常样本的表示方法, 从重构误差和稀疏度等角度检测异常适用于各种类型的图像, 通用性很好检测时间长, 而且需要额外的空间保存过完备的字典.[71-78]
    分类面构建建立分类面将现有的正常样本和潜在的异常样本进行区分通用性较好, 且速度较快各项参数的选择过程较为复杂[81-87]
    下载: 导出CSV

    表  3  基于深度学习的图像异常检测技术的分类和特点

    Table  3  The classification and characteristic of deep learning based image anomaly detection

    方法类别设计思路优点缺点参考文献
    距离度量将正常图像映射到指定区域内, 并减小正常特征之间距离, 根据待测图像的特征到聚类中心的距离进行异常检测模型结构简单, 适用范围广模型可能出现退化, 需要设计额外的辅助任务, 且无法准确定位异常区域[88-98]
    分类面构建通过几何变换增广现有数据, 直接训练分类模型并利用置信度来检测异常模型训练较为简单, 语义信息提取能力更强, 异常检测精度很高几何变换的操作在纹理图像等场景下并不适用[101-102]
    寻找与正常样本近似的图像作为负样本来训练二分类网络, 构建正常图像与潜在异常图像间的分类面应用场景广泛, 异常检测精度高需要精心设计损失函数和生成的负样本, 模型设计复杂[104-117]
    图像重构利用自编码器等模型学习正常图像的表达方式, 并根据待测图像的重构误差来进行异常检测训练阶段无需引入额外的样本, 且应用场景广泛, 速度较快一般的方法重构结果较为模糊, 且缺乏更为高效可靠的方法避免重构出异常区域[118-134]
    利用GAN来获得更为清晰的图像重构效果应用场景广泛, 异常区域定位精度高模型训练复杂, 而且缺乏理论上的保证[135-147]
    结合传统方法利用预训练的网络或者自编码器模型对图像进行特征提取, 在决策阶段利用传统方法进行异常检测相比传统方法精度更高通用性更好, 且速度较快在检测精度上略有不足[150-160]
    下载: 导出CSV

    表  4  图像异常检测常用数据集

    Table  4  Common datasets for image anomaly detection

    应用场景数据集名称参考文献
    工业布匹TILDA[161]
    PFID[162]
    金属MT[163]
    RSDD[164]
    NEU[165]
    纳米材料NanoTWICE[71]
    综合MVTec AD[146]
    医学大脑BraTS[167]
    视网膜AMD[168]
    高光谱混合AVIRIS[169]
    ABU[170]
    下载: 导出CSV

    表  5  各图像异常定位方法在MVTec AD上的性能

    Table  5  Performance of image anomaly localization methods on MVTec AD

    方法大致思路定位性能
    AUROCPRO-score
    AE [146]利用自编码器进行图像重构0.8170.790
    AnoGAN [15]利用GAN中的生成器进行图像重构0.7430.443
    Iterative Projection [134]在图像重构基础上采用迭代优化寻找最优的正常图像0.893
    AESc [172]利用蒙特卡洛对重构网络进行Dropout并利用预测不确定性进行异常定位0.86
    P-Net [16]在图像重构过程中添加对纹理结构的约束0.89
    Uninformed Students [97]联合考虑待测图特征到目标特征之间的距离和方差进行异常定位0.857
    CAVGA [144]在图像重构的基础上采用注意力图定位异常区域0.93
    FCDD [96]利用全卷积网络提取特征并以偏置项作为特征映射中心0.96
    Patch SVDD[173]计算待检图像片和最近似的正常图像片之间的距离进行异常定位0.957
    PaDiM [171]用预训练的网络进行特征提取, 利用多维高斯模型进行异常定位0.9750.921
    SPADE [174]寻找待测样本的K-近邻正常图像作为参考, 再通过距离度量进行异常检测0.9650.917
    下载: 导出CSV
  • [1] Chandola V, Banerjee A, Kumar V. Anomaly detection: a survey. ACM Computing Surveys, 2009, 41(3): 1-58
    [2] Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149
    [3] Tian Z, Shen C H, Chen H. FCOS: fully convolutional one-stage object detection. In: Proceedings of the 2019 IEEE International Conference on Computer Vision (ICCV). Seoul, Korea: IEEE, 2019. 9627−9636
    [4] Choi J, Kim C. Unsupervised detection of surface defects: a two-step approach. In: Proceedings of the 2012 IEEE International Conference on Image Processing (ICIP). Orlando, FL, USA: IEEE, 2012. 103−1040
    [5] Griffin L D, Caldwell M, Andrews J T A. 'Unexpected item in the bagging area': anomaly detection in x-ray security images. IEEE Transactions on Information Forensics & Security, 2019, 14(6): 1539-1553
    [6] Ahn E, Kumar A, Feng D. Unsupervised deep transfer feature learning for medical image classification. In: Proceedings of the 16th IEEE International Symposium on Biomedical Imaging (ISBI). Venice, Italy: IEEE, 2019. 1915−1918
    [7] Agyemang M, Barker K, Alhajj R. A comprehensive survey of numeric and symbolic outlier mining techniques. Intelligent Data Analysis, 2006, 10(6): 521-538 doi: 10.3233/IDA-2006-10604
    [8] Li C L, Gao G S, Liu Z F, Huang D, Xi J T. Defect detection for patterned fabric images based on GHOG and low-rank decomposition. IEEE Access, 2019, 7: 83962-83973 doi: 10.1109/ACCESS.2019.2925196
    [9] Lv C K, Zhang Z T, Shen F, Zhang F, Su H. A fast surface defect detection method based on background reconstruction. International Journal of Precision Engineering and Manufacturing, 2020, 21(3): 363-375 doi: 10.1007/s12541-019-00262-2
    [10] Wang J Z, Li Q Y, Gan J R, Yu H M, Yang X. Surface defect detection via entity sparsity pursuit with intrinsic priors. IEEE Transactions on Industrial Informatics, 2019, 16(1): 141-150
    [11] Yang H, Chen Y F, Song K Y, Yin Z P. Multiscale feature-clustering-based fully convolutional autoencoder for fast accurate visual inspection of texture surface defects. IEEE Transactions on Automation Science and Engineering, 2019, 16(3): 1450-1467 doi: 10.1109/TASE.2018.2886031
    [12] Bai X L, Fang Y M, Lin W S, Wang L P, Ju B F. Saliency-based defect detection in industrial images by using phase spectrum. IEEE Transactions on Industrial Informatics, 2014, 10(4): 2135-2145 doi: 10.1109/TII.2014.2359416
    [13] Zhou W J, Fei M R, Zhou H Y, Li K. A sparse representation based fast detection method for surface defect detection of bottle caps. Neurocomputing, 2014, 123: 406-414 doi: 10.1016/j.neucom.2013.07.038
    [14] Baur C, Wiestler B, Albarqouni S, Navab N. Bayesian skip-autoencoders for unsupervised hyperintense anomaly detection in high resolution brain MRI. In: Proceedings of the 17th IEEE International Symposium on Biomedical Imaging (ISBI). Iowa City, IA, USA: IEEE, 2020. 1905−1909
    [15] Schlegl T, Seeböck P, Waldstein S M, Schmidt-Erfurth U, Langs G. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Proceedings of the 25th International Conference on Information Processing in Medical Imaging (IPMI). Boone, NC, USA: Springer, 2017. 146−157
    [16] Zhou K, Xiao Y T, Yang J L, et al. Encoding structure-texture relation with p-net for anomaly detection in retinal images. In: Proceedings of the 16th European Conference on Computer Vision (ECCV). Virtual: Springer, 2020. 360−377
    [17] Wang N, Li B, Xu Q, Wang Y. Automatic ship detection in optical remote sensing images based on anomaly detection and SPP-PCANet. Remote Sensing, 2019, 11(1): 47-63
    [18] Jiang T, Li Y S, Xie W Y. Discriminative reconstruction constrained generative adversarial network for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4666-4679 doi: 10.1109/TGRS.2020.2965961
    [19] Jiang K, Xie W Y, Li Y S, Lei J, He G, Du Q. Semisupervised spectral learning with generative adversarial network for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 5224-5236 doi: 10.1109/TGRS.2020.2975295
    [20] Ehret T, Davy A, Morel J M, Delbracio M. Image anomalies: a review and synthesis of detection methods. Journal of Mathematical Imaging and Vision, 2019, 61(5): 710-743 doi: 10.1007/s10851-019-00885-0
    [21] Pang G S, Shen C H, Cao L B, Hengel A V D. Deep learning for anomaly detection: a review, ACM Computing Surveys, 2020, 1(1): 1−36
    [22] Chalapathy R, Chawla S. Deep learning for anomaly detection: a survey [Online], available: https://arxiv.org/abs/1901.03407, January 23, 2019
    [23] 陶显, 侯伟, 徐德. 基于深度学习的表面缺陷检测方法综述. 自动化学报, 2021, 47(05): 1017-1034

    Tao Xian, Hou Wei, Xu De. A survey of surface defect detection methods based on deep learning. Acta Automatica Sinica, 2021, 47(05): 1017-1034 (in Chinese)
    [24] Pimentel M A F, Clifton D A, Clifton L, Tarassenko L. A review of novelty detection. Signal Processing, 2014, 99: 215-249 doi: 10.1016/j.sigpro.2013.12.026
    [25] Hawkins D M. Identification of Outliers. London: Chapman and Hall, 1980. 1-12
    [26] Cook A, Mısırlı G, Fan Z. Anomaly detection for iot time-series data: a survey. IEEE Internet of Things Journal, 2019, 7(7): 6481-6494
    [27] Goldman A, Cohen I. Anomaly detection based on an iterative local statistics approach. Signal Processing, 2004, 84(7): 1225-1229 doi: 10.1016/j.sigpro.2004.04.004
    [28] Xiao H, Rasul K, Vollgraf R. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms [Online], available: https://arxiv.org/abs/1708.07747, August 25, 2017
    [29] 贾迪, 朱宁丹, 杨宁华, 吴思, 李玉秀, 赵明远. 图像匹配方法研究综述. 中国图像图形学报, 2019, 24(5): 677-699

    Jia Di, Zhu Ning-Dan, Yang Ning-Hua, Wu Si, Li Yu-Xiu, Zhao Ming-Yuan. Image matching methods. Journal of Image and Graphics, 24(5): 677-699 (in Chinese)
    [30] Loog M, Lauze F. The improbability of Harris interest points. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(6): 1141-1147 doi: 10.1109/TPAMI.2010.53
    [31] Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110 doi: 10.1023/B:VISI.0000029664.99615.94
    [32] Morel J M, Yu G. ASIFT: A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2009, 2(2): 438-469 doi: 10.1137/080732730
    [33] Rosten E, Porter R, Drummond T. Faster and better: a machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 32(1): 105-119
    [34] Verdie Y, Yi K, Fua P, Lepetit V. Tilde: A temporally invariant learned detector. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, 2015. 5279−5288
    [35] Wang Z H, Wu F C, Hu Z Y. MSLD: a robust descriptor for line matching. Pattern Recognition, 2009, 42(5): 941-953 doi: 10.1016/j.patcog.2008.08.035
    [36] Zhang L, Koch R. An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency. Journal of Visual Communication and Image Representation, 2013, 24(7): 794-805 doi: 10.1016/j.jvcir.2013.05.006
    [37] Xia M H, Liu B D. Image registration by "super-curves". IEEE Transactions on Image Processing, 2004, 13(5): 720-732 doi: 10.1109/TIP.2003.822611
    [38] Wolfson H J. On curve matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(5): 483-489 doi: 10.1109/34.55108
    [39] Pratt W K. Correlation techniques of image registration. IEEE Transactions on Aerospace and Electronic Systems, 1974, 10(3): 353-358
    [40] Leese J A, Novak C S, Clark B B. An automated technique for obtaining cloud motion from geosynchronous satellite data using cross correlation. Journal of Applied Meteorology and Climatology, 1971, 10(1): 118-132 doi: 10.1175/1520-0450(1971)010<0118:AATFOC>2.0.CO;2
    [41] Korman S, Reichman D, Tsur G, Avidan S. Fast-match: fast affine template matching. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Portland, OR, USA: IEEE, 2013. 2331−2338
    [42] Jia D, Cao J, Song W D, Tang X L, Zhu H. Colour FAST (CFAST) match: fast affine template matching for colour images. Electronics Letters, 2016, 52(14): 1220-1221 doi: 10.1049/el.2016.1331
    [43] Han X F, Leung T, Jia Y Q, Sukthankar R, Berg A C. Matchnet: unifying feature and metric learning for patch-based matching. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, 2015. 3279−3286
    [44] Balntas V, Johns E, Tang L, Mikolajczyk K. PN-Net: conjoined triple deep network for learning local image descriptors [Online], available: https://arxiv.org/abs/1601.05030v1, Jannuary 16, 2016
    [45] Chen W J, Ho J H, Mustapha K B, Chai T Y. A vision based system for anomaly detection and classification in additive manufacturing. In: Proceedings of the 2019 IEEE Conference on Sustainable Utilization and Development in Engineering and Technologies (CSUDET). Penang, Malaysia: IEEE, 2019. 87−92
    [46] Vaikundam S, Hung T Y, Chia L T. Anomaly region detection and localization in metal surface inspection. In: Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP). Phoenix, AZ, USA: IEEE, 2016. 759−763
    [47] Herwig J, Leßmann S, Bürger F, Pauli J. Adaptive anomaly detection within near-regular milling textures. In: Proceedings of the 8th International Symposium on Image and Signal Processing and Analysis (ISPA). Trieste, Italy: IEEE, 2013. 113−118
    [48] Tsai D M, Huang C K. Defect detection in electronic surfaces using template-based Fourier image reconstruction. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2018, 9(1): 163-172
    [49] Reed I S, Yu X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990, 38(10): 1760-1770 doi: 10.1109/29.60107
    [50] Du B, Zhang L P. Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 2010, 49(5): 1578-1589
    [51] Veracini T, Matteoli S, Diani M, Corshi G. Fully unsupervised learning of Gaussian mixtures for anomaly detection in hyperspectral imagery. In: Proceedings of the 2009 International Conference on Intelligent Systems Design and Applications. Pisa, Italy: IEEE, 2009. 596−601
    [52] Zhang H, Jin X T, Wu Q M J, Wang Y N, He Z D, Yang Y M. Automatic visual detection system of railway surface defects with curvature filter and improved Gaussian mixture model. IEEE Transactions on Instrumentation and Measurement, 2018, 67(7): 1593-1608 doi: 10.1109/TIM.2018.2803830
    [53] Schaum A. Joint subspace detection of hyperspectral targets. In: Proceedings of IEEE Aerospace Conference. Big Sky, MT, USA: IEEE, 2004. 1818−1824
    [54] Molero J M, Garzón E M, García I, Plaza A. Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 801-814 doi: 10.1109/JSTARS.2013.2238609
    [55] Mishne G, Cohen I. Multiscale anomaly detection using diffusion maps. IEEE Journal of selected topics in signal processing, 2012, 7(1): 111-123
    [56] 杨恩君, 廖义辉, 刘安东, 俞立. 基于低秩分解的织物瑕疵点检测. 纺织学报, 2020, 41(5): 72-78

    Yang En-Jun, Liao Yi-Hui, Liu An-Dong, Yu Li. Detection for fabric defects based on low-rank decomposition. Journal of Textile Research, 41(5): 72-78 (in Chinese)
    [57] Li C L, Liu C D, Gao G S, Liu Z F, Wang Y P. Robust low-rank decomposition of multi-channel feature matrices for fabric defect detection. Multimedia Tools and Applications, 2019, 78(6): 7321-7339 doi: 10.1007/s11042-018-6483-6
    [58] Zhou T, Tao D. Godec: randomized low-rank & sparse matrix decomposition in noisy case. In: Proceedings of the 28th International Conference on Machine Learning (ICML). Bellevue, Washington, USA: ACM, 2011. 33−40
    [59] Zhang Y X, Du B, Zhang L P, Wang S G. A low-rank and sparse matrix decomposition-based Mahalanobis distance method for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 2015, 54(3): 1376-1389
    [60] Wang J Z, Li Q Y, Gan J R, Yu H M. Fabric defect detection based on improved low-rank and sparse matrix decomposition. In: Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP). Beijing, China: IEEE, 2017. 2776−2780
    [61] Yang Y X, Zhang J Q, Song S Z, Zhang C, Liu D L. Low-rank and sparse matrix decomposition with orthogonal subspace projection-based background suppression for hyperspectral anomaly detection. IEEE Geoscience and Remote Sensing Letters, 2019, 17(8): 1378-1382
    [62] Guo C L, Ma Q, Zhang L M. Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Anchorage, AK, USA: IEEE, 2008. 1−8
    [63] Zhang H X, Guo Z D, Qi Z G, Wang J G. Research of glass defects detection based on DFT and optimal threshold method. In: Proceedings of the 2012 International Conference on Computer Science and Information Processing (CSIP). Xi'an, China: IEEE, 2012. 1044−1047
    [64] Liu H X, Zhou W, Kuang Q W, Cao L, Gao B. Defect detection of IC wafer based on spectral subtraction. IEEE Transactions on Semiconductor Manufacturing, 2010, 23(1): 141-147 doi: 10.1109/TSM.2009.2039185
    [65] Tsai D M, Huang T Y. Automated surface inspection for statistical textures. Image and Vision computing, 2003, 21(4): 307-323 doi: 10.1016/S0262-8856(03)00007-6
    [66] Tsai D M, Hsieh C Y. Automated surface inspection for directional textures. Image and Vision computing, 1999, 18(1): 49-62 doi: 10.1016/S0262-8856(99)00009-8
    [67] Hou X D, Zhang L Q. Saliency detection: a spectral residual approach. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Minneapolis, MN, USA: IEEE, 2007. 1−8
    [68] Li J, Levine M D, An X J, Xu X, He H G. Visual saliency based on scale-space analysis in the frequency domain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35(4): 996-1010
    [69] Aiger D, Talbot H. The phase only transform for unsupervised surface defect detection. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Francisco, CA, USA: IEEE, 2012. 215−232
    [70] Tsai D M, Kuo C C. Defect detection in inhomogeneously textured sputtered surfaces using 3D Fourier image reconstruction. Machine Vision and Applications, 2007, 18(6): 383-400 doi: 10.1007/s00138-007-0073-3
    [71] Carrera D, Manganini F, Boracchi G, Lanzarone E. Defect detection in SEM images of nanofibrous materials. IEEE Transactions on Industrial Informatics, 2016, 13(2): 551-561
    [72] Liang L Q, Li D, Fu X, Zhang W J. Touch screen defect inspection based on sparse representation in low resolution images. Multimedia Tools and Applications, 2016, 75(5): 2655-2666 doi: 10.1007/s11042-015-2559-8
    [73] Boracchi G, Carrera D, Wohlberg B. Novelty detection in images by sparse representations. In: Proceedings of the 2014 IEEE Symposium on Intelligent Embedded Systems (IES). Orlando, FL, USA: IEEE, 2014. 47−54
    [74] Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations & Trends in Machine Learning, 2010, 3(1): 1-122
    [75] Carrera D, Boracchi G, Foi A, Wohlberg B. Scale-invariant anomaly detection with multiscale group-sparse models. In: Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP). Phoenix, AZ, USA: IEEE, 2016. 3892−3896
    [76] Chen Y, Nasrabadi N M, Tran T D. Sparse representation for target detection in hyperspectral imagery. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3): 629-640 doi: 10.1109/JSTSP.2011.2113170
    [77] Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural networks, 2000, 13(4-5): 411-430 doi: 10.1016/S0893-6080(00)00026-5
    [78] Sezer O G, Erçil A, Ertuzun A. Using perceptual relation of regularity and anisotropy in the texture with independent component model for defect detection. Pattern Recognition, 2007, 40(1): 121-133 doi: 10.1016/j.patcog.2006.05.023
    [79] Schölkopf B, Williamson R C, Smola A J, Shawe-Taylor J, Platt J. Support vector method for novelty detection. In: Proceedings of the 1999 Advances in Neural Information Processing Systems (NIPS). Denver, Colorado, USA: MIT Press, 1999. 582−588
    [80] Amraee S, Vafaei A, Jamshidi K, Adibi P. Abnormal event detection in crowded scenes using one-class SVM. Signal, Image and Video Processing, 2018, 12(6): 1115-1123 doi: 10.1007/s11760-018-1267-z
    [81] Tax D M J, Duin R P W. Support vector data description. Machine Learning, 2004, 54(1): 45-66 doi: 10.1023/B:MACH.0000008084.60811.49
    [82] Wang J, Cherian A. Gods: generalized one-class discriminative subspaces for anomaly detection. In: Proceedings of 2019 IEEE International Conference on Computer Vision. Seoul, Korea: IEEE, 2019. 8201−8211
    [83] Azami M E, Bouet R, Jung J, Hammers A, Lartizien C. Combining multi-parametric MR images for the detection of epileptogenic lesions. In: Proceedings of the 12th IEEE International Symposium on Biomedical Imaging (ISBI). Brooklyn, NY, USA: IEEE, 2015. 122−125
    [84] Zhang L Y, Sun Y H, Meng D, Li X J. Anomaly detection for hyperspectral imagery based on incremental support vector data description. In: Proceedings of the 2010 International Conference on Multimedia Technology. Ningbo, China: IEEE, 2010. 1−4
    [85] Gurram P, Kwon H. Hyperspectral anomaly detection using an optimized support vector data description method. In: Proceedings of the 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Lisbon, Portugal: IEEE, 2011. 1−4
    [86] Liu Y H, Liu Y C, Chen Y J. Fast support vector data descriptions for novelty detection. IEEE Transactions on Neural Networks, 2010, 21(8): 1296-1313 doi: 10.1109/TNN.2010.2053853
    [87] Liu Y H, Lin S H, Hsueh Y L, Lee M J. Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble. Expert Systems with Applications, 2009, 36(2): 1978-1998 doi: 10.1016/j.eswa.2007.12.015
    [88] Ruff L, Vandermeulen R, Goernitz N, et al. Deep one-class classification. In: Proceedings of the 35th International Conference on Machine Learning. Stockholm, SWEDEN: ACM, 2018. 4393−4402
    [89] LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278-2324 doi: 10.1109/5.726791
    [90] Krizhevsky A. Learning Multiple Layers of Features From Tiny Images. Technical Report, Department of Computer Science, University of Toronto, CANADA, 2009
    [91] Kitamura S, Nonaka Y. Explainable anomaly detection via feature-based localization. In: Proceedings of the 28th International Conference on Artificial Neural Networks (ICANN). Munich, Germany: Springer, 2019. 408−419
    [92] Perera P, Patel V M. Learning deep features for one-class classification. IEEE Transactions on Image Processing, 2019, 28(11): 5450-5463 doi: 10.1109/TIP.2019.2917862
    [93] Wu P, Liu J, Shen F. A deep one-class neural network for anomalous event detection in complex scenes. IEEE Transactions on Neural Networks and Learning Systems, 2019, 31(7): 2609-2622
    [94] Bergman L, Hoshen Y. Classification-based anomaly detection for general data. In: Proceedings of the 2020 International Conference on Learning Representations (ICLR). Virtual: ICLR, 2020.
    [95] He X W, Zhou Y, Zhou Z C, Bai S, Bai X. Triplet-center loss for multi-view 3D object retrieval. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT, USA: IEEE, 2018. 1945−1954
    [96] Liznerski P, Ruff L, Vandermeulen R A, Franks B J, Kloft. M, Müller K R. Explainable deep one-class classification. In: Proceedings of 2021 International Conference on Learning Representations (ICLR). Virtual: ICLR, 2021.
    [97] Bergmann P, Fauser M, Sattlegger D, Steger C. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Virtual: IEEE, 2020. 4183−4191
    [98] Ruff L, Vandermeulen R A, Görnitz N, Binder A, Müller E, Müller K R, Kloft M. Deep semi-supervised anomaly detection. In: Proceedings of the 2020 International Conference on Learning Representations (ICLR). Virtual: ICLR, 2020.
    [99] Lee K, Lee K, Lee H, Shin J. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: Proceedings of the 2018 Advances in Neural Information Processing Systems (NIPS). Montréal, Canada: MIT Press, 2018. 7167−7177
    [100] Hendrycks D, Gimpel K. A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proceedings of the 2017 International Conference on Learning Representations (ICLR). Toulon, France: ICLR, 2017.
    [101] Golan I, El-Yaniv R. Deep anomaly detection using geometric transformations. In: Proceedings of the 2018 Advances in Neural Information Processing Systems (NIPS). Montréal, Canada: MIT Press, 2018. 9758−9769
    [102] Hendrycks D, Mazeika M, Kadavath S, Song D. Using self-supervised learning can improve model robustness and uncertainty. In: Proceedings of the 2019 Advances in Neural Information Processing Systems (NIPS). Vancouver, Canada: MIT Press, 2019. 15663−15674
    [103] Perera P, Patel V M. Deep transfer learning for multiple class novelty detection. In: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019. 11544−11552
    [104] Oza P, Patel V M. One-class convolutional neural network. IEEE Signal Processing Letters, 2018, 26(2): 277-281
    [105] Hendrycks D, Mazeika M, Dietterich T. Deep anomaly detection with outlier exposure. In: Proceedings of the 2019 International Conference on Learning Representations (ICLR). New Orleans, LA, USA: ICLR, 2019.
    [106] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Proceedings of the 2014 Advances in Neural Information Processing Systems (NIPS). Montréal, Canada: MIT Press, 2014. 2672−2680
    [107] Sabokrou M, Khalooei M, Fathy M, Adeli E. Adversarially learned one-class classifier for novelty detection. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT, USA: IEEE, 2018. 3379−3388
    [108] Sabokrou M, Fathy M, Zhao G, Adeli E. Deep end-to-end one-class classifier. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(2): 675-684 doi: 10.1109/TNNLS.2020.2979049
    [109] Yao Y, Rosasco L, Caponnetto A. On early stopping in gradient descent learning. Constructive Approximation, 2007, 26(2): 289-315 doi: 10.1007/s00365-006-0663-2
    [110] Yang Y, Hou C P, Lang Y, Yue G H, He Y. One-class classification using generative adversarial networks. IEEE Access, 2019, 7: 37970-37979 doi: 10.1109/ACCESS.2019.2905933
    [111] Chatillon P, Ballester C. History-based anomaly detector: an adversarial approach to anomaly detection. In: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys). London, UK: Springer, 2020. 761−776
    [112] Zaheer M Z, Lee J, Astrid M, Lee S I. Old is gold: Redefining the adversarially learned one-class classifier training paradigm. In: Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Virtual: IEEE, 2020. 14183−14193
    [113] Lim S K, Loo Y, Tran N T, Cheung N M, Roig G, Elovici Y. Doping: generative data augmentation for unsupervised anomaly detection with GAN. In: Proceedings of the 18th IEEE International Conference on Data Mining. Singapore, Singapore: IEEE, 2018. 1122−1127
    [114] Liu Y Z, Li Z, Zhou C, Jiang Y C, Sun J S, Wang M, He X N. Generative adversarial active learning for unsupervised outlier detection. IEEE Transactions on Knowledge and Data Engineering, 2019, 32(8): 1517-1528
    [115] Ngo P C, Winarto A A, Kou C K L, Park S, Akram F, Lee H K. Fence GAN: towards better anomaly detection. In: Proceedings of the 2019 IEEE International Conference on Tools with Artificial Intelligence (ICTAI). Portland, OR, USA: IEEE, 2019. 141−148
    [116] Schlachter P, Liao Y W, Yang B. Deep one-class classification using intra-class splitting. In: Proceedings of the 2019 IEEE Data Science Workshop (DSW). Minneapolis. MN, USA: IEEE, 2019. 100−104
    [117] Goyal S, Raghunathan A, Jain M, Simhardi H V, Jain P. DROCC: deep robust one-class classification. In: Proceedings of the 37th International Conference on Machine Learning. Virtual: ACM, 2020. 3711−3721
    [118] Hinton G E, Zemel R S. Autoencoders, minimum description length and Helmholtz free energy. In: Proceedings of the 1994 Advances in Neural Information Processing Systems (NIPS). Denver, CO, USA: MIT Press, 1994. 3−10
    [119] Mei S, Yang H, Yin Z P. An unsupervised-learning-based approach for automated defect inspection on textured surfaces. IEEE Transactions on Instrumentation and Measurement, 2018, 67(6): 1266-1277 doi: 10.1109/TIM.2018.2795178
    [120] Bergmann P, Löwe S, Fauser M, Sattlegger D, Steger C. Improving unsupervised defect segmentation by applying structural similarity to autoencoders [Online], available: https://arxiv.org/abs/1807.02011, July 5, 2018
    [121] Haselmann M, Gruber D P, Tabatabai P. Anomaly detection using deep learning based image completion. In: Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Orlando, FL, USA: IEEE, 2018. 1237−1242
    [122] Sakurada M, Yairi T. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2nd Workshop on Machine Learning for Sensory Data. Gold Coast, Australia: ACM, 2014. 4−11
    [123] Zhou C, Paffenroth R C. Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2017. 665−674
    [124] Lin T Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017. 2117−2125
    [125] Kwon G, Prabhushankar M, Temel D, AlRegib G. Backpropagated gradient representations for anomaly detection. In: Proceedings of the 16th European Conference on Computer Vision (ECCV). Virtual: Springer, 2020. 206−226
    [126] Zimmerer D, Petersen J, Kohl S A A, Maier-Hein K H. A case for the score: identifying image anomalies using variational autoencoder gradients [Online], available: https://arxiv.org/abs/1912.00003, November 28, 2019
    [127] Chu W H, Kitani K M. Neural batch sampling with reinforcement learning for semi-supervised anomaly detection. In: Proceedings of the 16th European Conference on Computer Vision (ECCV). Virtual: Springer, 2020. 751−766
    [128] Soukup D, Pinetz T. Reliably decoding autoencoders’ latent spaces for one-class learning image inspection scenarios. In: Proceedings of the Austrian Association for Pattern Recognition (OAGM/AAPR) Workshop. Hall/Tyrol, Austria: Verlag der TU Graz, 2018. 90−93
    [129] Abati D, Porrello A, Calderara S, Cucchiara R. Latent space autoregression for novelty detection. In: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019. 481−490
    [130] Perera P, Nallapati R, Xiang B. Ocgan: One-class novelty detection using gans with constrained latent representations. In: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019. 2898−2906
    [131] Tian H, Li F. Autoencoder-based fabric defect detection with cross-patch similarity. In: Proceedings of the 19th International Conference on Machine Vision Applications (MVA). Tokyo, Japan: IEEE, 2019. 1−6
    [132] Gong D, Liu L Q, Le V, et al. Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the 2019 IEEE International Conference on Computer Vision (ICCV). Seoul, Korea: IEEE, 2019. 1705−1714
    [133] Park H, Noh J, Ham B. Learning memory-guided normality for anomaly detection. In: Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Virtual: IEEE, 2020. 14372−14381
    [134] Dehaene D, Frigo O, Combrexelle S, Eline P. Iterative energy-based projection on a normal data manifold for anomaly localization. In: Proceedings of the 2020 International Conference on Learning Representations (ICLR). Online: ICLR, 2020.
    [135] Deecke L, Vandermeulen R, Ruff L, Mandt S, Kloft M. Image anomaly detection with generative adversarial networks. In: Proceedings of the 2018 Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Dublin, Ireland: Springer, 2018. 3−17
    [136] Lai Y T K, Hu J S, Tsai Y H, Chiu W Y. Industrial anomaly detection and one-class classification using generative adversarial networks. In: Proceedings of the 2018 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). Auckland, New Zealand: IEEE, 2018. 1444−1449
    [137] Pidhorskyi S, Almohsen R, Doretto G. Generative probabilistic novelty detection with adversarial autoencoders. In: Proceedings of the 2018 Advances in Neural Information Processing Systems (NIPS). Montréal, Canada: MIT Press, 2018. 6822−6833
    [138] Tuluptceva N, Bakker B, Fedulova I, Konushin A. Perceptual image anomaly detection [Online], available: https://arxiv.org/abs/1909.05904, September 12, 2019.
    [139] Zhao Z X, Li B, Dong R, Zhao P. A surface defect detection method based on positive samples. In: Proceedings of 2018 Pacific Rim International Conference on Artificial Intelligence (PRICAI). Nanjing, China: Springer, 2018. 473−481
    [140] Baur C, Wiestler B, Albarqouni S, Navab N. Deep autoencoding models for unsupervised anomaly segmentation in brain MR images. In: Proceedings of 2018 International MICCAI Brainlesion Workshop. Granada, Spain: Springer, 2018. 161−169
    [141] Akcay S, Atapour-Abarghouei A, Breckon T P. Ganomaly: Semi-supervised anomaly detection via adversarial training. In: Proceedings of the 14th Asian Conference on Computer Vision (ACCV). Perth, Australia: Springer, 2018. 622−637
    [142] Schlegl T, Seeböck P, Waldstein S M, Langs G, Schmidt-Erfurth U. F-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis, 2019, 54: 30-44 doi: 10.1016/j.media.2019.01.010
    [143] Tang T W, Kuo W H, Lan J H, Ding C F, Hsu H, Young H T. Anomaly detection neural network with dual auto-encoders GAN and its industrial inspection applications. Sensors, 2020, 20(12): 3336-3346 doi: 10.3390/s20123336
    [144] Venkataramanan S, Peng K C, Singh R V, Mahalanobis A. Attention guided anomaly localization in images. In: Proceedings of the 16th European Conference on Computer Vision (ECCV). Virtual: Springer, 2020. 485−503
    [145] Selvaraju R R, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE, 2017. 618−626
    [146] Bergmann P, Fauser M, Sattlegger D, Steger C. MVTec AD--a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019. 9592−9600
    [147] Lu Y, Yu F, Reddy M K K, Wamg Y. Few-shot scene-adaptive anomaly detection. In: Proceedings of the 16th European Conference on Computer Vision (ECCV). Virtual: Springer, 2020. 125−141
    [148] Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning (ICML). Sydney, Australia: ACM, 2017. 1126−1135
    [149] Xia Y, Zhang Y, Liu F, Shen W Yuille A L. Synthesize then compare: detecting failures and anomalies for semantic segmentation. In: Proceedings of the 16th European Conference on Computer Vision (ECCV). Virtual: Springer, 2020. 145−161
    [150] Gupta K, Bhavsar A, Sao A K. Detecting mitotic cells in HEp-2 images as anomalies via one class classifier. Computers in Biology and Medicine, 2019, 111: 1-13
    [151] Napoletano P, Piccoli F, Schettini R. Anomaly detection in nanofibrous materials by cnn-based self-similarity. Sensors, 2018, 18(1): 209-223 doi: 10.1109/JSEN.2017.2771313
    [152] Candès E J, Li X, Ma Y, Wright J. Robust principal component analysis?. Journal of the ACM, 2011, 58(3): 1-37
    [153] Mujeeb A, Dai W, Erdt M, Sourin A. One class based feature learning approach for defect detection using deep autoencoders. Advanced Engineering Informatics, 2019, 42: 100933 doi: 10.1016/j.aei.2019.100933
    [154] Sun J Y, Wang X Z, Xiong N X, Shao J. Learning sparse representation with variational auto-encoder for anomaly detection. IEEE Access, 2018, 6: 33353-33361 doi: 10.1109/ACCESS.2018.2848210
    [155] Alaverdyan Z, Jung J, Bouet R, Lartizien C. Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: application to epilepsy lesion screening. Medical Image Analysis, 2020, 60: 101618 doi: 10.1016/j.media.2019.101618
    [156] Burlina P, Joshi N, Wang I. Where's wally now? deep generative and discriminative embeddings for novelty detection. In: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019. 11507−11516
    [157] Liu F T, Ting K M, Zhou Z H. Isolation forest. In: Proceedings of the 8th IEEE International Conference on Data Mining. Pisa, Italy: IEEE, 2008. 413−422
    [158] Kozerawski J, Turk M. Clear: Cumulative learning for one-shot one-class image recognition. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, UT, USA: IEEE, 2018. 3446−3455
    [159] Zong B, Song Q, Min M R, et al. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: Proceedings of the 2018 International Conference on Learning Representations (ICLR). Vancouver, Canada, 2018. 1−19
    [160] Nie L H, Zhao L P, Li K Q. Glad: global and local anomaly detection. In: Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME). London, UK: IEEE, 2020. 1−6
    [161] TILDA textile texture database. [Online], available: https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html, August, 1996.
    [162] Ngan H Y T. Patterned fabric database [Online], available: https://ytngan.wordpress.com/codes/, April 3, 2017.
    [163] Huang Y. Magnetic tile defect datasets [Online], available: https://github.com/abin24/Magnetic-tile-defect-datasets., May 9, 2020.
    [164] Gan J R, Li Q Y, Wang J Z, Yu H M. A hierarchical extractor-based visual rail surface inspection system. IEEE Sensors Journal, 2017, 17(23): 7935-7944 doi: 10.1109/JSEN.2017.2761858
    [165] Song K C, Yan Y H. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Applied Surface Science, 2013, 285: 858-864 doi: 10.1016/j.apsusc.2013.09.002
    [166] Zhou S Y, Wu S Q, Liu H G, Lu Y, Hu N Z. Double low-rank and sparse decomposition for surface defect segmentation of steel sheet. Applied Sciences, 2018, 8(9): 1628-1643 doi: 10.3390/app8091628
    [167] Menze B H, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging, 2014, 34(10): 1993-2024
    [168] Farsiu S, Chiu S J, O'Connell R V, et al. Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology, 2014, 121(1): 162-172 doi: 10.1016/j.ophtha.2013.07.013
    [169] NASA, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) [Online], available: https://aviris.jpl.nasa.gov/index.html
    [170] Kang X. Airport-Beach-Urban dataset (ABU) [Online], available: http://xudongkang.weebly.com/data-sets.html
    [171] Defard T, Setkov A, Loesch A, Audigier R. PaDiM: a patch distribution modeling framework for anomaly detection and localization [Online], available: https://arxiv.org/abs/2011.08785, November 17, 2020.
    [172] Collin A S, De Vleeschouwer C. Improved anomaly detection by training an autoencoder with skip connections on images corrupted with stain-shaped noise. In: Proceedings of the 25th International Conference on Pattern Recognition (ICPR). Virtual: IEEE, 2020. 7915−7922
    [173] Yi J, Yoon S. Patch SVDD: patch-level SVDD for anomaly detection and segmentation. In: Proceedings of the 16th Asian Conference on Computer Vision (ACCV). Virtual: Springer, 2020. 1−22
    [174] Cohen N, Hoshen Y. Sub-image anomaly detection with deep pyramid correspondences [Online], available: https://arxiv.org/abs/2005.02357, February 3, 2021.
    [175] Howard A G, Zhu M, Chen B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications [Online], available: https://arxiv.org/abs/1704.04861, April 17, 2017.
  • 加载中
计量
  • 文章访问数:  124
  • HTML全文浏览量:  50
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-11-18
  • 录用日期:  2021-06-25
  • 网络出版日期:  2021-09-06

目录

    /

    返回文章
    返回