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Elsevier DOI
0401
BibRef
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0005
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IEEE DOI
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Gupta, L.[Lalit],
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Elsevier DOI
0806
Constraint satisfaction neural networks (CSNN); Segmentation;
Texture edge detection; Fuzzy-C means (FCM); Dynamic window
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Sahami, S.,
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DOI Link
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Längkvist, M.[Martin],
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DOI Link
1604
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Wei, Y.C.[Yun-Chao],
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Chen, Y.P.[Yun-Peng],
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Zhao, Y.[Yao],
Yan, S.C.[Shui-Cheng],
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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],
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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],
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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.[Liang-Chieh],
Zhu, Y.K.[Yu-Kun],
Papandreou, G.[George],
Schroff, F.[Florian],
Adam, H.[Hartwig],
Encoder-Decoder with Atrous Separable Convolution for Semantic Image
Segmentation,
ECCV18(VII: 833-851).
Springer DOI
1810
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],
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
Kang, B.,
Lee, Y.,
Nguyen, T.Q.,
Depth-Adaptive Deep Neural Network for Semantic Segmentation,
MultMed(20), No. 9, September 2018, pp. 2478-2490.
IEEE DOI
1809
convolution, feedforward neural nets, image colour analysis,
image segmentation, learning (artificial intelligence),
deep learning
BibRef
Chen, T.,
Lin, L.,
Wu, X.,
Xiao, N.,
Luo, X.,
Learning to Segment Object Candidates via Recursive Neural Networks,
IP(27), No. 12, December 2018, pp. 5827-5839.
IEEE DOI
1810
Proposals, Merging, Semantics, Feature extraction, Neural networks,
Measurement, Image segmentation, Object proposal generation,
deep learning
BibRef
Kemker, R.[Ronald],
Luu, R.,
Kanan, C.[Christopher],
Low-Shot Learning for the Semantic Segmentation of Remote Sensing
Imagery,
GeoRS(56), No. 10, October 2018, pp. 6214-6223.
IEEE DOI
1810
Feature extraction, Semantics, Image segmentation, Remote sensing,
Image reconstruction, Data models, Support vector machines,
semisupervised
BibRef
Kemker, R.[Ronald],
Salvaggio, C.[Carl],
Kanan, C.[Christopher],
Algorithms for semantic segmentation of multispectral remote sensing
imagery using deep learning,
PandRS(145), 2018, pp. 60-77.
Elsevier DOI
1810
Deep learning, Convolutional neural network,
Semantic segmentation, Multispectral, Unmanned aerial system, Synthetic imagery
BibRef
Chen, L.,
Bentley, P.,
Mori, K.,
Misawa, K.,
Fujiwara, M.,
Rueckert, D.,
DRINet for Medical Image Segmentation,
MedImg(37), No. 11, November 2018, pp. 2453-2462.
IEEE DOI
1811
Image segmentation, Computer architecture, Convolution, Training,
Medical diagnostic imaging, Standards,
abdominal organ segmentation
BibRef
Larsson, M.[Måns],
Arnab, A.[Anurag],
Zheng, S.[Shuai],
Torr, P.H.S.[Philip H.S.],
Kahl, F.[Fredrik],
Revisiting Deep Structured Models for Pixel-Level Labeling with
Gradient-Based Inference,
SIIMS(11), No. 4, 2018, pp. 2610-2628.
DOI Link
1901
BibRef
Ortiz, A.,
Granados, A.,
Fuentes, O.,
Kiekintveld, C.,
Rosario, D.,
Bell, Z.,
Integrated Learning and Feature Selection for Deep Neural Networks in
Multispectral Images,
PBVS18(1277-127709)
IEEE DOI
1812
Image segmentation, Semantics, Machine learning, Training,
Task analysis, Feature extraction, Neural networks
BibRef
McIntosh, L.,
Maheswaranathan, N.,
Sussillo, D.,
Shlens, J.,
Recurrent Segmentation for Variable Computational Budgets,
EfficientDeep18(1729-172909)
IEEE DOI
1812
Image segmentation, Semantics, Computer architecture,
Computational efficiency, Videos, Computational modeling, Network architecture
BibRef
Xu, X.,
Lu, Q.,
Yang, L.,
Hu, S.,
Chen, D.,
Hu, Y.,
Shi, Y.,
Quantization of Fully Convolutional Networks for Accurate Biomedical
Image Segmentation,
CVPR18(8300-8308)
IEEE DOI
1812
Quantization (signal), Training, Biomedical imaging,
Image segmentation, Uncertainty, Memory management, Neural networks
BibRef
Li, R.,
Li, K.,
Kuo, Y.,
Shu, M.,
Qi, X.,
Shen, X.,
Jia, J.,
Referring Image Segmentation via Recurrent Refinement Networks,
CVPR18(5745-5753)
IEEE DOI
1812
Image segmentation, Semantics, Natural languages, Task analysis,
Feature extraction, Logic gates, Training
BibRef
Berman, M.,
Triki, A.R.,
Blaschko, M.B.,
The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization
of the Intersection-Over-Union Measure in Neural Networks,
CVPR18(4413-4421)
IEEE DOI
1812
Indexes, Loss measurement, Optimization, Image segmentation,
Fasteners, Training, Semantics
BibRef
Briot, A.,
Viswanath, P.,
Yogamani, S.,
Analysis of Efficient CNN Design Techniques for Semantic Segmentation,
ECVW18(776-77609)
IEEE DOI
1812
Convolution, Computer architecture, Semantics, Hardware,
Quantization (signal), Kernel, Computational modeling
BibRef
Sankaranarayanan, S.,
Balaji, Y.,
Jain, A.,
Lim, S.N.,
Chellappa, R.,
Learning from Synthetic Data:
Addressing Domain Shift for Semantic Segmentation,
CVPR18(3752-3761)
IEEE DOI
1812
Task analysis, Semantics, Training, Image reconstruction, Generators,
Image segmentation, Data models
BibRef
Yu, C.,
Wang, J.,
Peng, C.,
Gao, C.,
Yu, G.,
Sang, N.,
Learning a Discriminative Feature Network for Semantic Segmentation,
CVPR18(1857-1866)
IEEE DOI
1812
Semantics, Task analysis, Feature extraction, Convolution,
Computer architecture, Computer vision, Benchmark testing
BibRef
Arnab, A.,
Miksik, O.,
Torr, P.H.S.,
On the Robustness of Semantic Segmentation Models to Adversarial
Attacks,
CVPR18(888-897)
IEEE DOI
1812
Robustness, Semantics, Image segmentation, Perturbation methods,
Task analysis, Neural networks, Training
BibRef
Wang, X.,
You, S.,
Li, X.,
Ma, H.,
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common
Object Features,
CVPR18(1354-1362)
IEEE DOI
1812
Image segmentation, Semantics, Training, Heating systems,
Task analysis, Feature extraction, Computer vision
BibRef
Shen, T.,
Lin, G.,
Shen, C.,
Reid, I.D.,
Bootstrapping the Performance of Webly Supervised Semantic
Segmentation,
CVPR18(1363-1371)
IEEE DOI
1812
Training, Image segmentation, Semantics, Knowledge engineering,
Noise measurement, Bidirectional control, Estimation
BibRef
Zhang, Z.,
Xie, C.,
Wang, J.,
Xie, L.,
Yuille, A.L.,
DeepVoting: A Robust and Explainable Deep Network for Semantic Part
Detection Under Partial Occlusion,
CVPR18(1372-1380)
IEEE DOI
1812
Semantics, Visualization, Training, Wheels, Proposals,
Feature extraction, Object detection
BibRef
Zlateski, A.,
Jaroensri, R.,
Sharma, P.,
Durand, F.,
On the Importance of Label Quality for Semantic Segmentation,
CVPR18(1479-1487)
IEEE DOI
1812
Computer vision, Pattern recognition
BibRef
Zhang, Y.,
Qiu, Z.,
Yao, T.,
Liu, D.,
Mei, T.,
Fully Convolutional Adaptation Networks for Semantic Segmentation,
CVPR18(6810-6818)
IEEE DOI
1812
Semantics, Image segmentation, Adaptation models, Visualization,
Task analysis, Games, Videos
BibRef
Saleh, F.S.[Fatemeh Sadat],
Aliakbarian, M.S.[Mohammad Sadegh],
Salzmann, M.[Mathieu],
Petersson, L.[Lars],
Alvarez, J.M.[Jose M.],
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation,
ECCV18(II: 86-103).
Springer DOI
1810
BibRef
Tang, M.[Meng],
Perazzi, F.[Federico],
Djelouah, A.[Abdelaziz],
Ben Ayed, I.[Ismail],
Schroers, C.[Christopher],
Boykov, Y.[Yuri],
On Regularized Losses for Weakly-supervised CNN Segmentation,
ECCV18(XVI: 524-540).
Springer DOI
1810
BibRef
Hu, T.[Tao],
Wang, Y.[Yao],
Chen, Y.S.[Yi-Song],
Lu, P.[Peng],
Wang, H.[Heng],
Wang, G.P.[Guo-Ping],
Sobel Heuristic Kernel for Aerial Semantic Segmentation,
ICIP18(3074-3078)
IEEE DOI
1809
Kernel, Semantics, Image segmentation, Image edge detection,
Neural networks, Detectors, Convolution, Semantic Segmentation, Edge Detection
BibRef
Siam, M.,
Gamal, M.,
Abdel-Razek, M.,
Yogamani, S.,
Jagersand, M.,
Zhang, H.,
A Comparative Study of Real-Time Semantic Segmentation for Autonomous
Driving,
ECVW18(700-70010)
IEEE DOI
1812
Convolution, Semantics, Computer architecture, Decoding,
Context modeling, Real-time systems, Image segmentation
BibRef
Siam, M.,
Gamal, M.,
Abdel-Razek, M.,
Yogamani, S.,
Jagersand, M.,
RTSeg: Real-Time Semantic Segmentation Comparative Study,
ICIP18(1603-1607)
IEEE DOI
1809
Computer architecture, Convolution, Semantics, Decoding,
Feature extraction, Benchmark testing, Real-time systems, realtime,
benchmarking framework
BibRef
Feng, Z.,
Yong, H.,
Xukun, S.,
GRANet: Global Refinement Atrous Convolutional Neural Network for
Semantic Scene Segmentation,
ICIP18(1568-1572)
IEEE DOI
1809
Semantics, Feature extraction, Convolution, Image segmentation,
Task analysis, Training, Convolutional neural networks,
Global Context
BibRef
Yang, W.,
Zhou, Q.,
Lu, J.,
Wu, X.,
Zhang, S.,
Latecki, L.J.,
Dense Deconvolutional Network for Semantic Segmentation,
ICIP18(1573-1577)
IEEE DOI
1809
Image segmentation, Training, Semantics, Decoding, Convolution,
Deconvolution, Feature extraction, Semantic Segmentation, FCNs
BibRef
Oudni, L.,
Vázquez, C.,
Coulombe, S.,
Motion Occlusions for Automatic Generation of Relative Depth Maps,
ICIP18(1538-1542)
IEEE DOI
1809
Optical imaging, Integrated optics, Image color analysis,
Estimation, Coherence, Interpolation, Image segmentation,
BibRef
Huang, Q.,
Xia, C.,
Li, S.,
Wang, Y.,
Song, Y.,
Kuo, C.C.J.,
Unsupervised Clustering Guided Semantic Segmentation,
WACV18(1489-1498)
IEEE DOI
1806
feature extraction, feedforward neural nets,
image classification, image representation, image segmentation,
Training
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.,
Yu, J.,
Mauch, L.,
Yang, B.,
Binary Segmentation Based Class Extension in Semantic Image
Segmentation Using Convolutional Neural Networks,
ICIP18(2232-2236)
IEEE DOI
1809
Image segmentation, Semantics, Training, Task analysis,
Computational modeling, Manuals, Convolutional neural networks,
convolutional neural networks
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 .