Abstract
The machine learning community has been overwhelmed by a plethora of deep learning--based approaches. Many challenging computer vision tasks, such as detection, localization, recognition, and segmentation of objects in an unconstrained environment, are being efficiently addressed by various types of deep neural networks, such as convolutional neural networks, recurrent networks, adversarial networks, and autoencoders. Although there have been plenty of analytical studies regarding the object detection or recognition domain, many new deep learning techniques have surfaced with respect to image segmentation techniques. This article approaches these various deep learning techniques of image segmentation from an analytical perspective. The main goal of this work is to provide an intuitive understanding of the major techniques that have made a significant contribution to the image segmentation domain. Starting from some of the traditional image segmentation approaches, the article progresses by describing the effect that deep learning has had on the image segmentation domain. Thereafter, most of the major segmentation algorithms have been logically categorized with paragraphs dedicated to their unique contribution. With an ample amount of intuitive explanations, the reader is expected to have an improved ability to visualize the internal dynamics of these processes.
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- Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, Sabine Süsstrunk, et al. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 11 (2012), 2274--2282. Google ScholarDigital Library
- Aseem Agarwala, Aaron Hertzmann, David H. Salesin, and Steven M. Seitz. 2004. Keyframe-based tracking for rotoscoping and animation. 23, 584--591. Google ScholarDigital Library
- Jamil Ahmad, Irfan Mehmood, and Sung Wook Baik. 2017. Efficient object-based surveillance image search using spatial pooling of convolutional features. Journal of Visual Communication and Image Representation 45 (2017), 62--76. Google ScholarDigital Library
- Fahim Irfan Alam, Jun Zhou, Alan Wee-Chung Liew, and Xiuping Jia. 2016. CRF learning with CNN features for hyperspectral image segmentation. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’16). IEEE, Los Alamitos, CA, 6890--6893.Google ScholarCross Ref
- Alberto Albiol, Luis Torres, and Edward J. Delp. 2001. An unsupervised color image segmentation algorithm for face detection applications. In Proceedings of the 2001 International Conference on Image Processing, Vol. 2. IEEE, Los Alamitos, CA, 681--684.Google Scholar
- Teresa Araújo, Guilherme Aresta, Eduardo Castro, José Rouco, Paulo Aguiar, Catarina Eloy, António Polónia, and Aurélio Campilho. 2017. Classification of breast cancer histology images using convolutional neural networks. PloS One 12, 6 (2017), e0177544.Google ScholarCross Ref
- Aamer Ather. 2009. A Quality Analysis of OpenStreetMap Data. Master’s Thesis. University College London, London, UK.Google Scholar
- Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 12 (2017), 2481--2495.Google ScholarCross Ref
- John Barlow, Steven Franklin, and Yvonne Martin. 2006. High spatial resolution satellite imagery, DEM derivatives, and image segmentation for the detection of mass wasting processes. Photogrammetric Engineering and Remote Sensing 72, 6 (2006), 687--692.Google ScholarCross Ref
- Serge Belongie, Chad Carson, Hayit Greenspan, and Jitendra Malik. 1998. Color-and texture-based image segmentation using EM and its application to content-based image retrieval. In Proceedings of the 6th International Conference on Computer Vision. IEEE, Los Alamitos, CA, 675--682. Google ScholarDigital Library
- Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle. 2007. Greedy layer-wise training of deep networks. In Advances in Neural Information Processing Systems. 153--160. Google ScholarDigital Library
- L. Sant’Anna Bins, L. M. Garcia Fonseca, G. J. Erthal, and F. Mitsuo Ii. 1996. Satellite imagery segmentation: A region growing approach. Simpósio Brasileiro de Sensoriamento Remoto 8, 1996 (1996), 677--680.Google Scholar
- Ali Borji. 2015. What is a salient object? A dataset and a baseline model for salient object detection. IEEE Transactions on Image Processing 24, 2 (2015), 742--756.Google ScholarDigital Library
- Ali Borji, Ming-Ming Cheng, Qibin Hou, Huaizu Jiang, and Jia Li. 2014. Salient object detection: A survey. arXiv:1411.5878.Google Scholar
- Ali Borji, Ming-Ming Cheng, Huaizu Jiang, and Jia Li. 2015. Salient object detection: A benchmark. IEEE Transactions on Image Processing 24, 12 (2015), 5706--5722.Google ScholarDigital Library
- Gabriel J. Brostow, Julien Fauqueur, and Roberto Cipolla. 2009. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters 30, 2 (2009), 88--97. Google ScholarDigital Library
- Nathan D. Cahill and Lawrence A. Ray. 2007. Method and system for compositing images to produce a cropped image. US Patent 7,162,102.Google Scholar
- Aaron Carass, Snehashis Roy, Amod Jog, Jennifer L. Cuzzocreo, Elizabeth Magrath, Adrian Gherman, et al. 2017. Longitudinal multiple sclerosis lesion segmentation data resource. Data in Brief 12 (2017), 346--350.Google ScholarCross Ref
- Lluis Castrejon, Kaustav Kundu, Raquel Urtasun, and Sanja Fidler. 2017. Annotating object instances with a polygon-rnn. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5230--5238.Google ScholarCross Ref
- Ping-Lin Chang and Wei-Guang Teng. 2007. Exploiting the self-organizing map for medical image segmentation. In Proceedings of the 20th IEEE International Symposium on Computer-Based Medical Systems (CBMS’07). IEEE, Los Alamitos, CA, 281--288. Google ScholarDigital Library
- Jianxu Chen, Lin Yang, Yizhe Zhang, Mark Alber, and Danny Z. Chen. 2016. Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation. In Advances in Neural Information Processing Systems. 3036--3044. Google ScholarDigital Library
- Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. 2014. Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv:1412.7062.Google Scholar
- Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. 2018. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 4 (2018), 834--848.Google ScholarCross Ref
- Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip H. S. Torr. 2015. Conditional random fields as recurrent neural networks. In Proceedings of the IEEE International Conference on Computer Vision. 1529--1537. Google ScholarDigital Library
- Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587.Google Scholar
- Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, and Alan L. Yuille. 2016. Attention to scale: Scale-aware semantic image segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3640--3649.Google Scholar
- Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv:1802.02611.Google Scholar
- Kuo-Sheng Cheng, Jzau-Sheng Lin, and Chi-Wu Mao. 1996. The application of competitive Hopfield neural network to medical image segmentation. IEEE Transactions on Medical Imaging 15, 4 (1996), 560--567.Google ScholarCross Ref
- Ming-Ming Cheng, Niloy J. Mitra, Xiaolei Huang, and Shi-Min Hu. 2014. SalientShape: Group saliency in image collections. Visual Computer 30, 4 (2014), 443--453. Google ScholarDigital Library
- Ming-Ming Cheng, Niloy J. Mitra, Xiaolei Huang, Philip H. S. Torr, and Shi-Min Hu. 2015. Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 3 (2015), 569--582. https://mmcheng.net/msra10k/.Google ScholarDigital Library
- Keh-Shih Chuang, Hong-Long Tzeng, Sharon Chen, Jay Wu, and Tzong-Jer Chen. 2006. Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics 30, 1 (2006), 9--15.Google ScholarCross Ref
- Dorin Comaniciu and Peter Meer. 1997. Robust analysis of feature spaces: Color image segmentation. In Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, Los Alamitos, CA, 750--755. Google ScholarDigital Library
- Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. 2016. The Cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3213--3223.Google ScholarCross Ref
- Jifeng Dai, Kaiming He, and Jian Sun. 2015. BoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In Proceedings of the IEEE International Conference on Computer Vision. 1635--1643. Google ScholarDigital Library
- Jifeng Dai, Kaiming He, and Jian Sun. 2016. Instance-aware semantic segmentation via multi-task network cascades. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3150--3158.Google ScholarCross Ref
- Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. 2016. R-FCN: Object detection via region-based fully convolutional networks. In Advances in Neural Information Processing Systems. 379--387. Google ScholarDigital Library
- Aritra Das, Swarnendu Ghosh, Ritesh Sarkhel, Sandipan Choudhuri, Nibaran Das, and Mita Nasipuri. 2019. Combining multilevel contexts of superpixel using convolutional neural networks to perform natural scene labeling. In Recent Developments in Machine Learning and Data Analytics. Springer, 297--306.Google Scholar
- M. Portes De Albuquerque, I. A. Esquef, and A. R. Gesualdi Mello. 2004. Image thresholding using Tsallis entropy. Pattern Recognition Letters 25, 9 (2004), 1059--1065. Google ScholarDigital Library
- Marleen de Bruijne, Bram van Ginneken, Max A. Viergever, and Wiro J. Niessen. 2004. Interactive segmentation of abdominal aortic aneurysms in CTA images. Medical Image Analysis 8, 2 (2004), 127--138.Google ScholarCross Ref
- Ilke Demir, Krzysztof Koperski, David Lindenbaum, Guan Pang, Jing Huang, Saikat Basu, Forest Hughes, Devis Tuia, and Ramesh Raskar. 2018. DeepGlobe 2018: A challenge to parse the earth through satellite images. arXiv:1805.06561.Google Scholar
- Yingzi Du, Emrah Arslanturk, Zhi Zhou, and Craig Belcher. 2011. Video-based noncooperative iris image segmentation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41, 1 (2011), 64--74. Google ScholarDigital Library
- Lixin Duan, Ivor W. Tsang, Dong Xu, and Tat-Seng Chua. 2009. Domain adaptation from multiple sources via auxiliary classifiers. In Proceedings of the 26th Annual International Conference on Machine Learning. ACM, New York, NY, 289--296. Google ScholarDigital Library
- Vincent Dumoulin and Francesco Visin. 2016. A guide to convolution arithmetic for deep learning. arXiv:1603.07285.Google Scholar
- Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman. 2010. The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88, 2 (2010), 303--338. Google ScholarDigital Library
- Clement Farabet, Camille Couprie, Laurent Najman, and Yann LeCun. 2013. Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 8 (2013), 1915--1929. Google ScholarDigital Library
- International Society for Photogrammetry and Remote Sensing. {n.d.}. ISPRS 2D Semantic Labeling Contest. Retrieved August 1, 2019 from http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.htmlGoogle Scholar
- Muhammad Moazam Fraz, Paolo Remagnino, Andreas Hoppe, Bunyarit Uyyanonvara, Alicja R. Rudnicka, Christopher G. Owen, and Sarah A. Barman. 2012. Blood vessel segmentation methodologies in retinal images—A survey. Computer Methods and Programs in Biomedicine 108, 1 (2012), 407--433. Google ScholarDigital Library
- Jordi Freixenet, Xavier Muñoz, David Raba, Joan Martí, and Xavier Cufí. 2002. Yet another survey on image segmentation: Region and boundary information integration. In Proceedings of the European Conference on Computer Vision. 408--422. Google ScholarDigital Library
- Nir Friedman and Stuart Russell. 1997. Image segmentation in video sequences: A probabilistic approach. In Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence. 175--181. Google ScholarDigital Library
- K.-S. Fu and J. K. Mui. 1981. A survey on image segmentation. Pattern Recognition 13, 1 (1981), 3--16.Google ScholarCross Ref
- Fabio Galasso, Naveen Shankar Nagaraja, Tatiana Jimenez Cardenas, Thomas Brox, and Bernt Schiele. 2013. A unified video segmentation benchmark: Annotation, metrics and analysis. In Proceedings of the IEEE International Conference on Computer Vision. 3527--3534. Google ScholarDigital Library
- Abhishek Gangwar and Akanksha Joshi. 2016. DeepIrisNet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition. In Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP’16). IEEE, Los Alamitos, CA, 2301--2305.Google ScholarCross Ref
- Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez, and Jose Garcia-Rodriguez. 2017. A review on deep learning techniques applied to semantic segmentation. arXiv:1704.06857.Google Scholar
- Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012. Are we ready for autonomous driving? The Kitti vision benchmark suite. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’12). IEEE, Los Alamitos, CA, 3354--3361. Google ScholarDigital Library
- Qichuan Geng, Zhong Zhou, and Xiaochun Cao. 2018. Survey of recent progress in semantic image segmentation with CNNs. Science China Information Sciences 61, 5 (2018), 051101.Google ScholarCross Ref
- Ross Girshick. 2015. Fast R-CNN. arXiv:1504.08083. Google ScholarDigital Library
- Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 580--587. Google ScholarDigital Library
- Stephen Gould, Richard Fulton, and Daphne Koller. 2009. Decomposing a scene into geometric and semantically consistent regions. In Proceedings of the 2009 IEEE 12th International Conference on Computer Vision. IEEE, Los Alamitos, CA, 1--8.Google ScholarCross Ref
- Xiao Han. 2017. Automatic liver lesion segmentation using a deep convolutional neural network method. arXiv:1704.07239. https://competitions.codalab.org/competitions/17094.Google Scholar
- Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik. 2011. Semantic contours from inverse detectors. In Proceedings of the International Conference on Computer Vision (ICCV’11). Google ScholarDigital Library
- Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017. Mask R-CNN. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV’17). IEEE, Los Alamitos, CA, 2980--2988.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 9 (2015), 1904--1916.Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.Google ScholarCross Ref
- Seunghoon Hong, Tackgeun You, Suha Kwak, and Bohyung Han. 2015. Online tracking by learning discriminative saliency map with convolutional neural network. In Proceedings of the International Conference on Machine Learning. 597--606. Google ScholarDigital Library
- Yang Hu, Andrea Soltoggio, Russell Lock, and Steve Carter. 2019. A fully convolutional two-stream fusion network for interactive image segmentation. Neural Networks 109 (2019), 31--42.Google ScholarCross Ref
- Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167.Google Scholar
- Humayun Irshad, Antoine Veillard, Ludovic Roux, and Daniel Racoceanu. 2014. Methods for nuclei detection, segmentation, and classification in digital histopathology: A review—Current status and future potential. IEEE Reviews in Biomedical Engineering 7 (2014), 97--114.Google ScholarCross Ref
- Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2017. Image-to-image translation with conditional adversarial networks. arXiv:1611.07004.Google Scholar
- Firas Ajil Jassim and Fawzi H. Altaani. 2013. Hybridization of Otsu method and median filter for color image segmentation. arXiv:1305.1052.Google Scholar
- Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, and Yoshua Bengio. 2017. The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’17). IEEE, Los Alamitos, CA, 1175--1183.Google ScholarCross Ref
- Cheng-Bin Jin, Shengzhe Li, Trung Dung Do, and Hakil Kim. 2015. Real-time human action recognition using CNN over temporal images for static video surveillance cameras. In Proceedings of the Pacific Rim Conference on Multimedia. 330--339. Google ScholarDigital Library
- A. H. Kam, T. T. Ng, N. G. Kingsbury, and W. J. Fitzgerald. 2000. Content based image retrieval through object extraction and querying. In Proceedings of the Workshop on Content-Based Access of Image and Visual Libraries (CBAIVL’00). IEEE, Los Alamitos, CA, 91. Google ScholarDigital Library
- Konstantinos Kamnitsas, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Daniel Rueckert, and Ben Glocker. 2017. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis 36 (2017), 61--78.Google ScholarCross Ref
- Asako Kanezaki. 2018. Unsupervised image segmentation by backpropagation. In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’18). IEEE, Los Alamitos, CA, 1543--1547.Google ScholarCross Ref
- Jiayin Kang, Xiao Li, Qingxian Luan, Jinzhu Liu, and Lequan Min. 2006. Dental plaque quantification using cellular neural network-based image segmentation. In Intelligent Computing in Signal Processing and Pattern Recognition. Springer, 797--802.Google Scholar
- Jiayin Kang and Wenjuan Zhang. 2009. Fingerprint segmentation using cellular neural network. In Proceedings of the International Conference on Computational Intelligence and Natural Computing (CINC’09), Vol. 2. IEEE, Los Alamitos, CA, 11--14. Google ScholarDigital Library
- Kai Kang and Xiaogang Wang. 2014. Fully convolutional neural networks for crowd segmentation. arXiv:1411.4464.Google Scholar
- Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980.Google Scholar
- Tao Kong, Anbang Yao, Yurong Chen, and Fuchun Sun. 2016. HyperNet: Towards accurate region proposal generation and joint object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 845--853.Google ScholarCross Ref
- Philipp Krähenbühl and Vladlen Koltun. 2011. Efficient inference in fully connected CRFs with Gaussian edge potentials. In Advances in Neural Information Processing Systems. 109--117. Google ScholarDigital Library
- Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. 2016. Semantic understanding of scenes through the ADE20K dataset. arXiv:1608.05442. Google ScholarDigital Library
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. 1097--1105. Google ScholarDigital Library
- Alfonso B. Labao and Prospero C. Naval. 2017. Weakly-labelled semantic segmentation of fish objects in underwater videos using a deep residual network. In Proceedings of the Asian Conference on Intelligent Information and Database Systems. 255--265.Google Scholar
- W. Ladys Law Skarbek and Andreas Koschan. 1994. Colour image segmentation a survey. IEEE Transactions on Circuits and Systems for Video Technology 14, 7 (1994).Google Scholar
- Rodney LaLonde and Ulas Bagci. 2018. Capsules for object segmentation. arXiv:1804.04241.Google Scholar
- Martin Längkvist, Andrey Kiselev, Marjan Alirezaie, and Amy Loutfi. 2016. Classification and segmentation of satellite orthoimagery using convolutional neural networks. Remote Sensing 8, 4 (2016), 329.Google ScholarCross Ref
- Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 11 (1998), 2278--2324.Google ScholarCross Ref
- Seong-Hun Lee, Min Su Cho, Kyomin Jung, and Jin Hyung Kim. 2010. Scene text extraction with edge constraint and text collinearity. In Proceedings of the 2010 International Conference on Pattern Recognition. IEEE, Los Alamitos, CA, 3983--3986. Google ScholarDigital Library
- Bastian Leibe, Edgar Seemann, and Bernt Schiele. 2005. Pedestrian detection in crowded scenes. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), Vol. 1. IEEE, Los Alamitos, CA, 878--885. Google ScholarDigital Library
- Dan Levi, Noa Garnett, Ethan Fetaya, and Israel Herzlyia. 2015. StixelNet: A deep convolutional network for obstacle detection and road segmentation. In Proceedings of the British Machine Vision Association (BMVC’15). 109.Google ScholarCross Ref
- Anat Levin, Dani Lischinski, and Yair Weiss. 2008. A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 2 (2008), 228--242. Google ScholarDigital Library
- Xiaoxiao Li, Ziwei Liu, Ping Luo, Chen Change Loy, and Xiaoou Tang. 2017. Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3193--3202.Google ScholarCross Ref
- Yin Li, Xiaodi Hou, Christof Koch, James M. Rehg, and Alan L. Yuille. 2014. The secrets of salient object segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 280--287. Google ScholarDigital Library
- Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, and Yichen Wei. 2016. Fully convolutional instance-aware semantic segmentation. arXiv:1611.07709.Google Scholar
- Wen-Nung Lie. 1995. Automatic target segmentation by locally adaptive image thresholding. IEEE Transactions on Image Processing 4, 7 (1995), 1036--1041. Google ScholarDigital Library
- Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid. 2017. RefineNet: Multi-path refinement networks for high-resolution semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17).Google ScholarCross Ref
- Min Lin, Qiang Chen, and Shuicheng Yan. 2013. Network in network. arXiv:1312.4400.Google Scholar
- Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common objects in context. In Proceedings of the European Conference on Computer Vision. 740--755.Google Scholar
- Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen AWM van der Laak, Bram van Ginneken, and Clara I Sánchez. 2017. A survey on deep learning in medical image analysis. Medical Image Analysis 42 (2017), 60--88.Google ScholarCross Ref
- Nianfeng Liu, Haiqing Li, Man Zhang, Jing Liu, Zhenan Sun, and Tieniu Tan. 2016. Accurate iris segmentation in non-cooperative environments using fully convolutional networks. In Proceedings of the 2016 International Conference on Biometrics (ICB’16). IEEE, Los Alamitos, CA, 1--8.Google ScholarCross Ref
- Sifei Liu, Shalini De Mello, Jinwei Gu, Guangyu Zhong, Ming-Hsuan Yang, and Jan Kautz. 2017. Learning affinity via spatial propagation networks. In Advances in Neural Information Processing Systems. 1520--1530. Google ScholarDigital Library
- Ying Liu, Dengsheng Zhang, Guojun Lu, and Wei-Ying Ma. 2007. A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40, 1 (2007), 262--282. Google ScholarDigital Library
- Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen-Change Loy, and Xiaoou Tang. 2015. Semantic image segmentation via deep parsing network. In Proceedings of the IEEE International Conference on Computer Vision. 1377--1385. Google ScholarDigital Library
- Christos P. Loizou, Víctor Murray, Marios S. Pattichis, Ioannis Seimenis, Marios Pantziaris, and Constantinos S. Pattichis. 2011. Multiscale amplitude-modulation frequency-modulation (AM--FM) texture analysis of multiple sclerosis in brain MRI images. IEEE Transactions on Information Technology in Biomedicine 15, 1 (2011), 119--129. Google ScholarDigital Library
- Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3431--3440.Google ScholarCross Ref
- Karen López-Linares, Nerea Lete, Luis Kabongo, Mario Ceresa, Gregory Maclair, Ainhoa García-Familiar, Iván Macía, and Miguel Ángel González Ballester. 2018. Comparison of regularization techniques for DCNN-based abdominal aortic aneurysm segmentation. In Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI’18). IEEE, Los Alamitos, CA, 864--867.Google ScholarCross Ref
- Ping Lu, Livia Barazzetti, Vimal Chandran, Kate Gavaghan, Stefan Weber, Nicolas Gerber, and Mauricio Reyes. 2018. Highly accurate facial nerve segmentation refinement from CBCT/CT imaging using a super-resolution classification approach. IEEE Transactions on Biomedical Engineering 65, 1 (2018), 178--188.Google ScholarCross Ref
- Pauline Luc, Camille Couprie, Soumith Chintala, and Jakob Verbeek. 2016. Semantic segmentation using adversarial networks. arXiv:1611.08408.Google Scholar
- Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat, and Pierre Alliez. 2017. Can semantic labeling methods generalize to any city? The INRIA aerial image labeling benchmark. In Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing (IGARSS’17).Google ScholarCross Ref
- Oskar Maier, Bjoern H. Menze, Janina von der Gablentz, Levin Häni, Mattias P. Heinrich, Matthias Liebrand, et al. 2017. ISLES 2015—A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Medical Image Analysis 35 (2017), 250--269.Google ScholarCross Ref
- Rupesh Mandal and Nupur Choudhury. 2016. Automatic video surveillance for theft detection in ATM machines: An enhanced approach. In Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom’16). IEEE, Los Alamitos, CA, 2821--2826.Google Scholar
- Kevis-Kokitsi Maninis, Sergi Caelles, Jordi Pont-Tuset, and Luc Van Gool. 2018. Deep extreme cut: From extreme points to object segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 616--625.Google ScholarCross Ref
- Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbeláez, and Luc Van Gool. 2016. Convolutional oriented boundaries. In Proceedings of the European Conference on Computer Vision. 580--596.Google ScholarCross Ref
- D. Martin, C. Fowlkes, D. Tal, and J. Malik. 2001. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings of the 8th International Conference on Computer Vision, Vol. 2. 416--423.Google Scholar
- Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber. 2011. Stacked convolutional auto-encoders for hierarchical feature extraction. In Proceedings of the International Conference on Artificial Neural Networks. 52--59. Google ScholarDigital Library
- L. R. Medsker and L. C. Jain. 2001. Recurrent Neural Networks: Design and Applications. CRC Press, Boca Raton, FL. Google ScholarDigital Library
- B. M. Mehtre, N. N. Murthy, S. Kapoor, and B. Chatterjee. 1987. Segmentation of fingerprint images using the directional image. Pattern Recognition 20, 4 (1987), 429--435. Google ScholarDigital Library
- Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, et al. 2015. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging 34, 10 (2015), 1993--2024.Google ScholarCross Ref
- Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. 2017. Scene parsing through ADE20K dataset. In Proceedings of the Conference on Computer Vision and Pattern Recognition.Google ScholarCross Ref
- Andrew Merlino, Daryl Morey, and Mark Maybury. 1997. Broadcast news navigation using story segmentation. In Proceedings of the 5th ACM International Conference on Multimedia. ACM, New York, NY, 381--391. Google ScholarDigital Library
- Filippo Molinari, Guang Zeng, and Jasjit S. Suri. 2010. A state of the art review on intima-media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound. Computer Methods and Programs in Biomedicine 100, 3 (2010), 201--221. Google ScholarDigital Library
- Takayasu Moriya, Holger R. Roth, Shota Nakamura, Hirohisa Oda, Kai Nagara, Masahiro Oda, and Kensaku Mori. 2018. Unsupervised segmentation of 3D medical images based on clustering and deep representation learning. In Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, Vol. 10578. International Society for Optics and Photonics, Bellingham, WA, 1057820.Google Scholar
- T. Nathan Mundhenk, Daniel Ho, and Barry Y. Chen. 2018. Improvements to context based self-supervised learning. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR’18).Google Scholar
- Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML’10). 807--814. Google ScholarDigital Library
- Ahmed Nassar, Karim Amer, Reda El Hakim, and Mohamed El Helw. 2018. A deep CNN-based framework for enhanced aerial imagery registration with applications to UAV geolocalization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 1513--1523.Google ScholarCross Ref
- Gerhard Neuhold, Tobias Ollmann, S. Rota Bulo, and Peter Kontschieder. 2017. The Mapillary Vistas dataset for semantic understanding of street scenes. In Proceedings of the International Conference on Computer Vision (ICCV’17). 22--29.Google ScholarCross Ref
- Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. 2015. Learning deconvolution network for semantic segmentation. In Proceedings of the IEEE International Conference on Computer Vision. 1520--1528. Google ScholarDigital Library
- Mehdi Noroozi and Paolo Favaro. 2016. Unsupervised learning of visual representations by solving jigsaw puzzles. In Proceedings of the European Conference on Computer Vision. 69--84.Google ScholarCross Ref
- Christine M. Onyango and John A. Marchant. 2001. Physics-based colour image segmentation for scenes containing vegetation and soil. Image and Vision Computing 19, 8 (2001), 523--538.Google ScholarCross Ref
- Anisha Pal, Shourya Jaiswal, Swarnendu Ghosh, Nibaran Das, and Mita Nasipuri. {n.d.}. SegFast: A faster SqueezeNet based semantic image segmentation technique using depth-wise separable convolutions. In Proceedings of the 11th Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP’18). ACM, New York, NY, 7.Google Scholar
- Nikhil R. Pal and Sankar K. Pal. 1993. A review on image segmentation techniques. Pattern Recognition 26, 9 (1993), 1277--1294.Google ScholarCross Ref
- George Papandreou, Liang-Chieh Chen, Kevin P. Murphy, and Alan L. Yuille. 2015. Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation. In Proceedings of the IEEE International Conference on Computer Vision. 1742--1750. Google ScholarDigital Library
- Adam Paszke, Abhishek Chaurasia, Sangpil Kim, and Eugenio Culurciello. 2016. ENet: A deep neural network architecture for real-time semantic segmentation. arXiv:1606.02147.Google Scholar
- Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros. 2016. Context encoders: Feature learning by inpainting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2536--2544.Google Scholar
- Chao Peng, Xiangyu Zhang, Gang Yu, Guiming Luo, and Jian Sun. 2017. Large kernel matters—Improve semantic segmentation by global convolutional network. arXiv:1703.02719.Google Scholar
- Pedro O. Pinheiro, Ronan Collobert, and Piotr Dollár. 2015. Learning to segment object candidates. In Advances in Neural Information Processing Systems. 1990--1998. Google ScholarDigital Library
- Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, and Piotr Dollár. 2016. Learning to refine object segments. In Proceedings of the European Conference on Computer Vision. 75--91.Google ScholarCross Ref
- Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbeláez, Alex Sorkine-Hornung, and Luc Van Gool. 2017. The 2017 Davis Challenge on video object segmentation. arXiv:1704.00675.Google Scholar
- Prasanna Porwal, Samiksha Pachade, Ravi Kamble, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, et al. 2018. Diabetic retinopathy: Segmentation and grading challenge workshop. In Proceedings of the IEEE International Symposium on Biomedical Imaging(ISBI’18).https://idrid.grand-challenge.org/organizers/Google Scholar
- Huafeng Qin and Mounim A. El-Yacoubi. 2017. Deep representation-based feature extraction and recovering for finger-vein verification. IEEE Transactions on Information Forensics and Security 12, 8 (2017), 1816--1829. Google ScholarDigital Library
- P. Radau, Y. Lu, K. Connelly, G. Paul, A. Dick, and G. Wright. 2009. Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS Journal 49 (2009).Google Scholar
- Anurag Ranjan, Varun Jampani, Kihwan Kim, Deqing Sun, Jonas Wulff, and Michael J. Black. 2018. Adversarial collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation. arXiv:1805.09806.Google Scholar
- Mahdyar Ravanbakhsh, Moin Nabi, Hossein Mousavi, Enver Sangineto, and Nicu Sebe. 2016. Plug-and-play CNN for crowd motion analysis: An application in abnormal event detection. arXiv:1610.00307.Google Scholar
- Mengye Ren and Richard S. Zemel. 2017. End-to-end instance segmentation with recurrent attention. arXiv:1605.09410.Google Scholar
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems. 91--99. Google ScholarDigital Library
- Bernardino Romera-Paredes and Philip Hilaire Sean Torr. 2016. Recurrent instance segmentation. In Proceedings of the European Conference on Computer Vision. 312--329.Google ScholarCross Ref
- Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 234--241.Google ScholarCross Ref
- German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M. Lopez. 2016. The Synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3234--3243.Google Scholar
- Brandon Rothrock, Ryan Kennedy, Chris Cunningham, Jeremie Papon, Matthew Heverly, and Masahiro Ono. 2016. Spoc: Deep learning-based terrain classification for Mars Rover missions. In Proceedings of the AIAA SPACE 2016 Conference. 5539.Google ScholarCross Ref
- David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1986. Learning representations by back-propagating errors. Nature 323, 6088 (1986), 533.Google ScholarCross Ref
- Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. 2017. Dynamic routing between capsules. In Advances in Neural Information Processing Systems. 3856--3866. Google ScholarDigital Library
- N. Senthilkumaran and R. Rajesh. 2009. Edge detection techniques for image segmentation—A survey of soft computing approaches. International Journal of Recent Trends in Engineering 1, 2 (2009), 250--254.Google Scholar
- Neeraj Sharma and Lalit M. Aggarwal. 2010. Automated medical image segmentation techniques. Journal of Medical Physics/Association of Medical Physicists of India 35, 1 (2010), 3.Google Scholar
- Jianbo Shi and Jitendra Malik. 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 8 (2000), 888--905. Google ScholarDigital Library
- Jianping Shi, Qiong Yan, Li Xu, and Jiaya Jia. 2016. Hierarchical image saliency detection on extended CSSD. IEEE Transactions on Pattern Analysis and Machine Intelligence 38, 4 (2016), 717--729. Google ScholarDigital Library
- Jamie Shotton, John Winn, Carsten Rother, and Antonio Criminisi. 2006. TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In Proceedings of the European Conference on Computer Vision. 1--15. Google ScholarDigital Library
- Margarida Silveira, Jacinto C. Nascimento, Jorge S. Marques, André R. S. Marçal, Teresa Mendonça, Syogo Yamauchi, Junji Maeda, and Jorge Rozeira. 2009. Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE Journal of Selected Topics in Signal Processing 3, 1 (2009), 35--45.Google ScholarCross Ref
- Yan Song, Yuemei Zhu, Guangliang Li, Chen Feng, Bo He, and Tianhong Yan. 2017. Side scan sonar segmentation using deep convolutional neural network. In Proceedings of the 2017 OCEANS--Anchorage Conference. IEEE, Los Alamitos, CA, 1--4.Google Scholar
- Joes Staal, Michael D. Abràmoff, Meindert Niemeijer, Max A. Viergever, and Bram Van Ginneken. 2004. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23, 4 (2004), 501--509.Google ScholarCross Ref
- Tamás Szirányi, Károly László, László Czúni, and Francesco Ziliani. 1999. Object oriented motion-segmentation for video-compression in the CNN-UM. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology 23, 2-3 (1999), 479--496. Google ScholarDigital Library
- Khang Siang Tan and Nor Ashidi Mat Isa. 2011. Color image segmentation using histogram thresholding—Fuzzy c-means hybrid approach. Pattern Recognition 44, 1 (2011), 1--15. Google ScholarDigital Library
- Orlando José Tobias and Rui Seara. 2002. Image segmentation by histogram thresholding using fuzzy sets. IEEE Transactions on Image Processing 11, 12 (2002), 1457--1465. Google ScholarDigital Library
- Michael Treml, José Arjona-Medina, Thomas Unterthiner, Rupesh Durgesh, Felix Friedmann, Peter Schuberth, et al. 2016. Speeding up semantic segmentation for autonomous driving. In Proceedings of the MLITS NIPS Workshop.Google Scholar
- Wei-Chih Tu, Ming-Yu Liu, Varun Jampani, Deqing Sun, Shao-Yi Chien, Ming-Hsuan Yang, and Jan Kautz. 2018. Learning superpixels with segmentation-aware affinity loss. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 568--576.Google ScholarCross Ref
- Jasper R. R. Uijlings, Koen E. A. Van De Sande, Theo Gevers, and Arnold W. M. Smeulders. 2013. Selective search for object recognition. International Journal of Computer Vision 104, 2 (2013), 154--171. Google ScholarDigital Library
- Koen E. A. Van de Sande, Jasper R. R. Uijlings, Theo Gevers, and Arnold W. M. Smeulders. 2011. Segmentation as selective search for object recognition. In Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV’11). IEEE, Los Alamitos, CA, 1879--1886. Google ScholarDigital Library
- Bram Van Ginneken, Mikkel B. Stegmann, and Marco Loog. 2006. Segmentation of anatomical structures in chest radiographs using supervised methods: A comparative study on a public database. Medical Image Analysis 10, 1 (2006), 19--40.Google ScholarCross Ref
- G. Varma, A. Subramanian, A. Namboodiri, M. Chandraker, and C. V. Jawahar. 2018. IDD: A dataset for exploring problems of autonomous navigation in unconstrained environments. arXiv:1811.10200.Google Scholar
- Andreas Veit, Tomas Matera, Lukas Neumann, Jiri Matas, and Serge Belongie. 2016. Coco-text: Dataset and benchmark for text detection and recognition in natural images. arXiv:1601.07140.Google Scholar
- David L. Vilarino, Diego Cabello, and Victor M. Brea. 2002. An analogic CNN-algorithm of pixel level snakes for tracking and surveillance tasks. In Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications (CNNA’02). IEEE, Los Alamitos, CA, 84--91.Google Scholar
- Kai Wang, Boris Babenko, and Serge Belongie. 2011. End-to-end scene text recognition. In Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV’11). IEEE, Los Alamitos, CA, 1457--1464. Google ScholarDigital Library
- Guo-Qing Wei, Klaus Arbter, and Gerd Hirzinger. 1997. Real-time visual servoing for laparoscopic surgery. Controlling robot motion with color image segmentation. IEEE Engineering in Medicine and Biology Magazine 16, 1 (1997), 40--45.Google ScholarCross Ref
- Xide Xia and Brian Kulis. 2017. W-Net: A deep model for fully unsupervised image segmentation. arXiv:1711.08506.Google Scholar
- Jieqiong Xu, Guoyu Wang, and Feifei Sun. 2013. A novel method for detecting and tracking vehicles in traffic-image sequence. In Proceedings of the 5th International Conference on Digital Image Processing (ICDIP’13), Vol. 8878. 88782P.Google ScholarCross Ref
- Ning Xu, Linjie Yang, Yuchen Fan, Dingcheng Yue, Yuchen Liang, Jianchao Yang, and Thomas Huang. 2018. YouTube-VOS: A large-scale video object segmentation benchmark. arXiv:1809.03327.Google Scholar
- Chuan Yang, Lihe Zhang, Huchuan Lu, Xiang Ruan, and Ming-Hsuan Yang. 2013. Saliency detection via graph-based manifold ranking. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13). IEEE, Los Alamitos, CA, 3166--3173. Google ScholarDigital Library
- Fisher Yu and Vladlen Koltun. 2015. Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122.Google Scholar
- Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell. 2018. BDD100K: A diverse driving video database with scalable annotation tooling. arXiv:1805.04687.Google Scholar
- Jiangye Yuan, Shaun S. Gleason, and Anil M. Cheriyadat. 2013. Systematic benchmarking of aerial image segmentation. IEEE Geoscience and Remote Sensing Letters 10, 6 (2013), 1527--1531.Google ScholarCross Ref
- Matthew D. Zeiler and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In Proceedings of the European Conference on Computer Vision. 818--833.Google Scholar
- Darko Zikic, Yani Ioannou, Matthew Brown, and Antonio Criminisi. 2014. Segmentation of brain tumor tissues with convolutional neural networks. In Proceedings of the MICCAI Workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS'14). 36--39.Google Scholar
- Xiaohang Zhan, Xingang Pan, Ziwei Liu, Dahua Lin, and Chen Change Loy. 2019. Self-supervised learning via conditional motion propagation. arXiv:1903.11412.Google Scholar
- Qi Zhang, Sally A. Goldman, Wei Yu, and Jason E. Fritts. 2002. Content-based image retrieval using multiple-instance learning. In Proceedings of the 19th International Conference on Machine Learning (ICML’02), Vol. 2. 682--689. Google ScholarDigital Library
- Richard Zhang, Phillip Isola, and Alexei A. Efros. 2016. Colorful image colorization. In Proceedings of the European Conference on Computer Vision. 649--666.Google Scholar
- Bo Zhao, Jiashi Feng, Xiao Wu, and Shuicheng Yan. 2017. A survey on deep learning-based fine-grained object classification and semantic segmentation. International Journal of Automation and Computing 14, 2 (2017), 119--135. Google ScholarDigital Library
- Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. 2017. Pyramid scene parsing network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 2881--2890.Google ScholarCross Ref
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- Understanding Deep Learning Techniques for Image Segmentation
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