8.3.4.2 Neural Networks for Segmentation

Tsao, E.C.K.[Eric Chen-Kuo], Lin, W.C.[Wei-Chung], Chen, C.T.[Chin-Tu],
Constraint satisfaction neural networks for image recognition,
PR(26), No. 4, April 1993, pp. 553-567.
Elsevier DOI 0401
BibRef
Earlier: A2, A1, A3:
Constraint Satisfaction Neural Networks for Image Segmentation,
PR(25), No. 7, July 1992, pp. 679-693.
Elsevier DOI BibRef

Chen, K.S., Tsay, D.H., Huang, W.P., Tzeng, Y.C.,
Remote Sensing Image Segmentation Using a Kalman Filter-Trained Neural-Network,
IJIST(7), No. 2, Summer 1996, pp. 141-148. 9607
BibRef

Cheng, K.S.[Kuo-Sheng], Lin, J.S.[Jzau-Sheng], Mao, C.W.[Chi-Wu],
The Application of Competitive Hopfield Neural Network to Medical Image Segmentation,
MedImg(15), No. 4, August 1996, pp. 560-567.
IEEE Top Reference. 0203
BibRef

Ziemke, T.,
Radar Image Segmentation Using Recurrent Artificial Neural Networks,
PRL(17), No. 4, April 4 1996, pp. 319-334. 9605
BibRef

Routa, S.[Saroj], Seethalakshmy, A.G., Srivastava, P.[Pramod], Majumdar, J.[Jharna],
Multimodal Image Segmentation Using a Modified Hopfield Neural Network,
PR(31), No. 6, June 1998, pp. 743-750.
Elsevier DOI 9806
BibRef

Venkatesh, Y.V., Rishikesh, N.,
Self-Organizing Neural Networks Based on Spatial Isomorphism for Active Contour Modeling,
PR(33), No. 7, July 2000, pp. 1239-1250.
Elsevier DOI 0005
BibRef

Venkatesh, Y.V., Raja, S.K., Ramya, N.,
Multiple contour extraction from graylevel images using an artificial neural network,
IP(15), No. 4, April 2006, pp. 892-899.
IEEE DOI 0604
BibRef

Gupta, L.[Lalit], Mangai, U.G.[Utthara Gosa], Das, S.[Sukhendu],
Integrating region and edge information for texture segmentation using a modified constraint satisfaction neural network,
IVC(26), No. 8, 1 August 2008, pp. 1106-1117.
Elsevier DOI 0806
Constraint satisfaction neural networks (CSNN); Segmentation; Texture edge detection; Fuzzy-C means (FCM); Dynamic window BibRef

Sahami, S., Shayesteh, M.G.,
Bi-level image compression technique using neural networks,
IET-IPR(6), No. 5, 2012, pp. 496-506.
DOI Link 1210
BibRef

Längkvist, M.[Martin], Kiselev, A.[Andrey], Alirezaie, M.[Marjan], Loutfi, A.[Amy],
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks,
RS(8), No. 4, 2016, pp. 329.
DOI Link 1604
BibRef

Wei, Y.C.[Yun-Chao], Liang, X.D.[Xiao-Dan], Chen, Y.P.[Yun-Peng], Jie, Z.[Zequn], Xiao, Y.H.[Yan-Hui], Zhao, Y.[Yao], Yan, S.C.[Shui-Cheng],
Learning to segment with image-level annotations,
PR(59), No. 1, 2016, pp. 234-244.
Elsevier DOI 1609
Semantic segmentation. with CNNs BibRef

Wei, Y.C.[Yun-Chao], Liang, X.D.[Xiao-Dan], Chen, Y.P.[Yun-Peng], Shen, X., Cheng, M.M., Feng, J., Zhao, Y.[Yao], Yan, S.C.[Shui-Cheng],
STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation,
PAMI(39), No. 11, November 2017, pp. 2314-2320.
IEEE DOI 1710
Benchmark testing, Image segmentation, Neural networks, Object detection, Semantics, Training, Semantic segmentation, convolutional neural network, weakly-supervised learning BibRef

Wei, Y.C.[Yun-Chao], Feng, J., Liang, X.D.[Xiao-Dan], Cheng, M.M., Zhao, Y.[Yao], Yan, S.C.[Shui-Cheng],
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach,
CVPR17(6488-6496)
IEEE DOI 1711
Head, Heating systems, Image segmentation, Proposals, Semantics, Training BibRef

Dong, L.[Le], Feng, N.[Ning], Zhang, Q.[Qianni],
LSI: Latent semantic inference for natural image segmentation,
PR(59), No. 1, 2016, pp. 282-291.
Elsevier DOI 1609
Image Segmentation BibRef

Lu, Z.W.[Zhi-Wu], Fu, Z.Y.[Zhen-Yong], Xiang, T.[Tao], Han, P.[Peng], Wang, L.W.[Li-Wei], Gao, X.[Xin],
Learning from Weak and Noisy Labels for Semantic Segmentation,
PAMI(39), No. 3, March 2017, pp. 486-500.
IEEE DOI 1702
Computational modeling BibRef

Li, A.[Aoxue], Lu, Z.W.[Zhi-Wu], Wang, L.W.[Li-Wei], Han, P.[Peng], Wen, J.R.[Ji-Rong],
Large-Scale Sparse Learning From Noisy Tags for Semantic Segmentation,
Cyber(48), No. 1, January 2018, pp. 253-263.
IEEE DOI 1801
Image segmentation, Matrix decomposition, Noise measurement, Noise reduction, Semantics, Symmetric matrices, Visualization, semantic segmentation BibRef

Cao, Y., Shen, C., Shen, H.T.,
Exploiting Depth From Single Monocular Images for Object Detection and Semantic Segmentation,
IP(26), No. 2, February 2017, pp. 836-846.
IEEE DOI 1702
estimation theory BibRef

Zhang, M.[Mi], Hu, X.Y.[Xiang-Yun], Zhao, L.[Like], Lv, Y.[Ye], Luo, M.[Min], Pang, S.Y.[Shi-Yan],
Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Ghodrati, A.[Amir], Diba, A.[Ali], Pedersoli, M.[Marco], Tuytelaars, T.[Tinne], Van Gool, L.J.[Luc J.],
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers,
IJCV(124), No. 2, September 2017, pp. 115-131.
Springer DOI 1708
BibRef
Earlier:
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers,
ICCV15(2578-2586)
IEEE DOI 1602
Aggregates BibRef

Hong, S., Kwak, S., Han, B.,
Weakly Supervised Learning with Deep Convolutional Neural Networks for Semantic Segmentation: Understanding Semantic Layout of Images with Minimum Human Supervision,
SPMag(34), No. 6, November 2017, pp. 39-49.
IEEE DOI 1712
Benchmark testing, Image recognition, Image segmentation, Machine learning, Neural networks, Semantics, Visualization BibRef

Holliday, A.[Andrew], Barekatain, M.[Mohammadamin], Laurmaa, J.[Johannes], Kandaswamy, C.[Chetak], Prendinger, H.[Helmut],
Speedup of deep learning ensembles for semantic segmentation using a model compression technique,
CVIU(164), No. 1, 2017, pp. 16-26.
Elsevier DOI 1801
Semantic segmentation BibRef

Romera, E., Álvarez, J.M., Bergasa, L.M., Arroyo, R.,
ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation,
ITS(19), No. 1, January 2018, pp. 263-272.
IEEE DOI 1801
Computer architecture, Image segmentation, Kernel, Real-time systems, Semantics, Two dimensional displays, semantic segmentation BibRef

Chen, L.C.[Liang-Chieh], Papandreou, G.[George], Kokkinos, I.[Iasonas], Murphy, K.P.[Kevin P.], Yuille, A.L.[Alan L.],
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,
PAMI(40), No. 4, April 2018, pp. 834-848.
IEEE DOI 1804
BibRef
Earlier: A2, A1, A4, A5, Only:
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation,
ICCV15(1742-1750)
IEEE DOI 1602
convolution, feature extraction, feedforward neural nets, image segmentation, learning (artificial intelligence), semantic segmentation. Benchmark testing BibRef

Chen, L.C., Barron, J.T., Papandreou, G., Murphy, K.P., Yuille, A.L.,
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform,
CVPR16(4545-4554)
IEEE DOI 1612
BibRef

Konishi, S.[Scott], Scott Konishi, A., Yuille, A.L.,
Statistical Cues for Domain Specific Image Segmentation with Performance Analysis,
CVPR00(I: 125-132).
IEEE DOI 0005
BibRef

Guo, Y.M.[Yan-Ming], Liu, Y.[Yu], Georgiou, T.[Theodoros], Lew, M.S.[Michael S.],
A review of semantic segmentation using deep neural networks,
MultInfoRetr(8), No. 2, June 2018, pp. 87-93.
Springer DOI 1805
Survey, Semantic Segmentation. BibRef

Nakajima, Y.[Yoshikatsu], Saito, H.[Hideo],
Simultaneous Object Segmentation and Recognition by Merging CNN Outputs from Uniformly Distributed Multiple Viewpoints,
IEICE(E101-D), No. 5, May 2018, pp. 1308-1316.
WWW Link. 1805
BibRef

Wang, C.Y.[Chun-Yan], Xu, A.[Aigong], Li, X.[Xiaoli],
Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Wang, C.Y.[Chun-Yan], Xu, A.[Aigong], Li, C.[Chao], Zhao, X.M.[Xue-Mei],
Interval Type-2 Fuzzy Based Neural Network For High Resolution Remote Sensing Image Segmentation,
ISPRS16(B7: 385-391).
DOI Link 1610
BibRef

Liu, Z.W.[Zi-Wei], Li, X.X.[Xiao-Xiao], Luo, P.[Ping], Loy, C.C.[Chen Change], Tang, X.[Xiaoou],
Deep Learning Markov Random Field for Semantic Segmentation,
PAMI(40), No. 8, August 2018, pp. 1814-1828.
IEEE DOI 1807
BibRef
Earlier: A2, A1, A3, A4, A5:
Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade,
CVPR17(6459-6468)
IEEE DOI 1711
BibRef
Earlier: A1, A2, A3, A4, A5:
Semantic Image Segmentation via Deep Parsing Network,
ICCV15(1377-1385)
IEEE DOI 1602
Computational modeling, Computer architecture, Image segmentation, Markov random fields, Neural networks, convolutional neural network. Adaptation models, Cows, Real-time systems, Semantics, Training. Computational efficiency BibRef

Nigam, I., Huang, C., Ramanan, D.,
Ensemble Knowledge Transfer for Semantic Segmentation,
WACV18(1499-1508)
IEEE DOI 1806
image segmentation, learning (artificial intelligence), aerial drone robotics, aerial scenes, aerial segmentation, Visualization BibRef

Richmond, D.[David], Kainmueller, D.[Dagmar], Yang, M.[Michael], Myers, E.[Eugene], Rother, C.[Carsten],
Mapping Auto-context Decision Forests to Deep ConvNets for Semantic Segmentation,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Nekrasov, V.[Vladimir], Ju, J.[Janghoon], Choi, J.[Jaesik],
Global Deconvolutional Networks for Semantic Segmentation,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Jiang, Y., Chi, Z.,
A Fully-Convolutional Framework for Semantic Segmentation,
DICTA17(1-7)
IEEE DOI 1804
image classification, image segmentation, learning (artificial intelligence), deep learning technique, Semantics BibRef

Fu, J., Liu, J., Wang, Y., Lu, H.,
Densely connected deconvolutional network for semantic segmentation,
ICIP17(3085-3089)
IEEE DOI 1803
Convergence, Image segmentation, Semantics, Spatial resolution, Stacking, Training, Deconvolutional Network, Dense Connection, Semantic Segmentation BibRef

Chu, J., Xiao, X., Meng, G., Wang, L., Pan, C.,
Learnable contextual regularization for semantic segmentation of indoor scene images,
ICIP17(1267-1271)
IEEE DOI 1803
Computer architecture, Convolution, Image segmentation, Kernel, Semantics, Task analysis, Training, Contextual constraints, Semantic segmentation BibRef

Liu, Y., Lew, M.S.,
Improving the discrimination between foreground and background for semantic segmentation,
ICIP17(1272-1276)
IEEE DOI 1803
Computational modeling, Image segmentation, Proposals, Semantics, Standards, Task analysis, Training, Fully Convolutional Networks, Semantic Segmentation BibRef

Qi, X., Liao, R., Jia, J., Fidler, S., Urtasun, R.,
3D Graph Neural Networks for RGBD Semantic Segmentation,
ICCV17(5209-5218)
IEEE DOI 1802
feature extraction, graph theory, image classification, image representation, image segmentation, BibRef

Souly, N., Spampinato, C., Shah, M.,
Semi Supervised Semantic Segmentation Using Generative Adversarial Network,
ICCV17(5689-5697)
IEEE DOI 1802
feature extraction, image classification, image segmentation, learning (artificial intelligence), semantic networks, Visualization BibRef

Sickert, S.[Sven], Denzler, J.[Joachim],
Semantic Segmentation of Outdoor Areas Using 3D Moment Invariants and Contextual Cues,
GCPR17(165-176).
Springer DOI 1711
BibRef

Morley, D., Foroosh, H.,
Improving RANSAC-Based Segmentation through CNN Encapsulation,
CVPR17(2661-2670)
IEEE DOI 1711
Encapsulation, Feature extraction, Image edge detection, Image segmentation, Training BibRef

Ke, T.W., Maire, M., Yu, S.X.,
Multigrid Neural Architectures,
CVPR17(4067-4075)
IEEE DOI 1711
Computer architecture, Convolution, Image segmentation, Routing, Semantics, Standards BibRef

Cohen, G., Weinshall, D.,
Hidden Layers in Perceptual Learning,
CVPR17(5349-5357)
IEEE DOI 1711
Biological system modeling, Computational modeling, Convolution, Image segmentation, Training, Visualization BibRef

Roy, A.[Anirban], Todorovic, S.[Sinisa],
Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation,
CVPR17(7282-7291)
IEEE DOI 1711
BibRef
Earlier:
A Multi-scale CNN for Affordance Segmentation in RGB Images,
ECCV16(IV: 186-201).
Springer DOI 1611
Gaussian distribution, Image segmentation, Labeling, Neurons, Semantics, Training, Visualization BibRef

Nissen, M.S.[Malte S.], Krause, O.[Oswin], Almstrup, K.[Kristian], Kjærulff, S.[Søren], Nielsen, T.T.[Torben T.], Nielsen, M.[Mads],
Convolutional Neural Networks for Segmentation and Object Detection of Human Semen,
SCIA17(I: 397-406).
Springer DOI 1706
BibRef

Schneider, L.[Lukas], Jasch, M.[Manuel], Fröhlich, B.[Björn], Weber, T.[Thomas], Franke, U.[Uwe], Pollefeys, M.[Marc], Rätsch, M.[Matthias],
Multimodal Neural Networks: RGB-D for Semantic Segmentation and Object Detection,
SCIA17(I: 98-109).
Springer DOI 1706
BibRef

García, G.M., Husain, F., Schulz, H., Frintrop, S., Torras, C., Behnke, S.,
Semantic segmentation priors for object discovery,
ICPR16(549-554)
IEEE DOI 1705
Computer vision, Image color analysis, Image segmentation, Neural networks, Proposals, Semantics BibRef

Wang, C., Mauch, L., Guo, Z., Yang, B.,
On semantic image segmentation using deep convolutional neural network with shortcuts and easy class extension,
IPTA16(1-6)
IEEE DOI 1703
image segmentation BibRef

Mousavian, A., Pirsiavash, H., KošeckŽá, J.,
Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks,
3DV16(611-619)
IEEE DOI 1701
Computer architecture BibRef

Visin, F., Romero, A., Cho, K., Matteucci, M., Ciccone, M., Kastner, K., Bengio, Y., Courville, A.,
ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation,
DeepLearn-C16(426-433)
IEEE DOI 1612
BibRef

Jarrar, M., Kerkeni, A., Abdallah, A.B., Bedoui, M.H.,
MLP Neural Network Classifier for Medical Image Segmentation,
CGiV16(88-93)
IEEE DOI 1608
image classification BibRef

Hernández, J.[Juanita], Gómez, W.[Wilfrido],
Automatic Tuning of the Pulse-Coupled Neural Network Using Differential Evolution for Image Segmentation,
MCPR16(157-166).
Springer DOI 1608
BibRef

Murdock, C.[Calvin], de la Torre, F.[Fernando],
Additive Component Analysis,
CVPR17(673-681)
IEEE DOI 1711
BibRef
Earlier:
Semantic Component Analysis,
ICCV15(1484-1492)
IEEE DOI 1602
Additives, Image reconstruction, Kernel, Machine learning, Manifolds, Optimization, Principal component analysis. Feature extraction. decomposition of images into semantic components. BibRef

Noh, H., Hong, S., Han, B.,
Learning Deconvolution Network for Semantic Segmentation,
ICCV15(1520-1528)
IEEE DOI 1602
Deconvolution BibRef

Lin, D., Dai, J., Jia, J., He, K., Sun, J.,
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation,
CVPR16(3159-3167)
IEEE DOI 1612
BibRef

Dai, J., He, K., Sun, J.,
Instance-Aware Semantic Segmentation via Multi-task Network Cascades,
CVPR16(3150-3158)
IEEE DOI 1612
BibRef
And:
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation,
ICCV15(1635-1643)
IEEE DOI 1602
Erbium BibRef

Pathak, D., Krahenbuhl, P., Darrell, T.J.,
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation,
ICCV15(1796-1804)
IEEE DOI 1602
Convolutional codes BibRef

Safar, S.[Simon], Yang, M.H.[Ming-Hsuan],
Learning shape priors for object segmentation via neural networks,
ICIP15(1835-1839)
IEEE DOI 1512
Object segmentation; convolutional neural networks; shape priors BibRef

Gidaris, S.[Spyridon], Komodakis, N.[Nikos],
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization,
BMVC16(xx-yy).
HTML Version. 1805
BibRef
And:
LocNet: Improving Localization Accuracy for Object Detection,
CVPR16(789-798)
IEEE DOI 1612
BibRef
Earlier:
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model,
ICCV15(1134-1142)
IEEE DOI 1602
Biological system modeling BibRef

Rota Bulo, S.[Samuel], Kontschieder, P.[Peter],
Neural Decision Forests for Semantic Image Labelling,
CVPR14(81-88)
IEEE DOI 1409
neural network; random forest; semantic image labelling BibRef

Masci, J.[Jonathan], Giusti, A.[Alessandro], Ciresan, D.C.[Dan C.], Fricout, G.[Gabriel], Schmidhuber, J.[Jurgen],
A fast learning algorithm for image segmentation with max-pooling convolutional networks,
ICIP13(2713-2717)
IEEE DOI 1402
Convolutional Network BibRef

Giusti, A.[Alessandro], Ciresan, D.C.[Dan C.], Masci, J.[Jonathan], Gambardella, L.M.[Luca M.], Schmidhuber, J.[Jurgen],
Fast image scanning with deep max-pooling convolutional neural networks,
ICIP13(4034-4038)
IEEE DOI 1402
Biomedical Imaging BibRef

del Campo-Becerra, G.D.M.[Gustavo D. Martín], Yañez-Vargas, J.I.[Juan I.], López-Ruíz, J.A.[Josué A.],
Texture Analysis of Mean Shift Segmented Low-Resolution Speckle-Corrupted Fractional SAR Imagery through Neural Network Classification,
CASI14(998-1005).
Springer DOI 1411
BibRef

Yazdanpanah, A.P.[Ali Pour], Regentova, E.E.[Emma E.], Mandava, A.K.[Ajay Kumar], Ahmad, T.[Touqeer],
Sky Segmentation by Fusing Clustering with Neural Networks,
ISVC13(II:663-672).
Springer DOI 1311
BibRef

Andersen, J.D.[Jens D.],
Image Decomposition by Radial Basis Functions,
SCIA03(749-754).
Springer DOI 0310
BibRef

Matsui, K.[Kazuhiro], Kosugi, Y.[Yukio],
Image Segmentation by Neural-net Classifiers with Genetic Selection of Feature Indices,
ICIP99(I:524-528).
IEEE DOI BibRef 9900

Zong, X., Meyer-Baese, A., and Laine, A.,
Multiscale Segmentation Through a Radial Basis Neural Network,
ICIP97(III: 400-403).
IEEE DOI BibRef 9700

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Other Complete Systems .