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
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
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.L.[Xiao-Li],
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
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.[Mĺns],
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
Wu, G.M.[Guang-Ming],
Guo, Y.M.[Yi-Min],
Song, X.Y.[Xiao-Ya],
Guo, Z.L.[Zhi-Ling],
Zhang, H.R.[Hao-Ran],
Shi, X.D.[Xiao-Dan],
Shibasaki, R.[Ryosuke],
Shao, X.W.[Xiao-Wei],
A Stacked Fully Convolutional Networks with Feature Alignment
Framework for Multi-Label Land-cover Segmentation,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link
1905
BibRef
Wang, Q.,
Yuan, C.,
Liu, Y.,
Learning Deep Conditional Neural Network for Image Segmentation,
MultMed(21), No. 7, July 2019, pp. 1839-1852.
IEEE DOI
1906
Feature extraction, Object segmentation, Visualization,
Brain modeling, Context modeling, Convolutional neural networks,
conditional Boltzmann machines
BibRef
He, C.[Chu],
Fang, P.[Peizhang],
Zhang, Z.[Zhi],
Xiong, D.[Dehui],
Liao, M.S.[Ming-Sheng],
An End-to-End Conditional Random Fields and Skip-Connected Generative
Adversarial Segmentation Network for Remote Sensing Images,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link
1907
BibRef
Ghosh, S.[Swarnendu],
Das, N.[Nibaran],
Das, I.[Ishita],
Maulik, U.[Ujjwal],
Understanding Deep Learning Techniques for Image Segmentation,
Surveys(52), No. 4, September 2019, pp. Article No 73.
DOI Link
1912
BibRef
Han, Y.M.[Yong-Ming],
Zhang, S.[Shuheng],
Geng, Z.[Zhiqing],
Wei, Q.[Qin],
Ouyang, Z.[Zhi],
Level set based shape prior and deep learning for image segmentation,
IET-IPR(14), No. 1, January 2020, pp. 183-191.
DOI Link
1912
BibRef
Wang, S.[Sherrie],
Chen, W.[William],
Xie, S.M.[Sang Michael],
Azzari, G.[George],
Lobell, D.B.[David B.],
Weakly Supervised Deep Learning for Segmentation of Remote Sensing
Imagery,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link
2001
BibRef
Yu, J.,
Blaschko, M.B.,
The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses,
PAMI(42), No. 3, March 2020, pp. 735-748.
IEEE DOI
2002
Fasteners, Risk management, Optimization, Training,
Complexity theory, Task analysis, Indexes, Lovász extension,
Jaccard index score
BibRef
Karimi, D.,
Salcudean, S.E.,
Reducing the Hausdorff Distance in Medical Image Segmentation With
Convolutional Neural Networks,
MedImg(39), No. 2, February 2020, pp. 499-513.
IEEE DOI
2002
Image segmentation, Biomedical imaging, Training,
Sensitivity, convolutional neural networks
BibRef
Li, K.[Kun],
Hu, X.Y.[Xiang-Yun],
Jiang, H.[Huiwei],
Shu, Z.[Zhen],
Zhang, M.[Mi],
Attention-Guided Multi-Scale Segmentation Neural Network for
Interactive Extraction of Region Objects from High-Resolution
Satellite Imagery,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link
2003
BibRef
Gu, X.B.[Xian-Bin],
Deng, J.D.[Jeremiah D.],
A multi-feature bipartite graph ensemble for image segmentation,
PRL(131), 2020, pp. 98-104.
Elsevier DOI
2004
Image segmentation, Feature fusion, Bipartite graph, Spectral clustering
BibRef
Guo, Z.K.[Zheng-Kun],
Song, Y.[Yong],
Zhao, Y.F.[Yu-Fei],
Yang, X.[Xin],
Wang, F.N.[Feng-Ning],
An adaptive infrared image segmentation method based on fusion SPCNN,
SP:IC(87), 2020, pp. 115905.
Elsevier DOI
2007
Infrared image segmentation, Pulse coupled neural network,
Adaptive parameter setting, Output selection
BibRef
Han, L.[Lili],
Li, S.J.[Shu-Juan],
Ren, P.X.[Peng-Xin],
Xue, D.D.[Ding-Dan],
Block cosparsity overcomplete learning transform image segmentation
algorithm based on burr model,
IET-IPR(14), No. 10, August 2020, pp. 2074-2080.
DOI Link
2008
BibRef
Lin, D.Y.[Dong-Yun],
Li, Y.Q.[Yi-Qun],
Nwe, T.L.[Tin Lay],
Dong, S.[Sheng],
Oo, Z.M.[Zaw Min],
RefineU-Net: Improved U-Net with progressive global feedbacks and
residual attention guided local refinement for medical image
segmentation,
PRL(138), 2020, pp. 267-275.
Elsevier DOI
2010
U-Net, Medical image segmentation,
Progressive global feedbacks, Local refinement, Residual attention gate
BibRef
Ye, L.,
Liu, Z.,
Wang, Y.,
Dual Convolutional LSTM Network for Referring Image Segmentation,
MultMed(22), No. 12, December 2020, pp. 3224-3235.
IEEE DOI
2011
Image segmentation, Visualization, Decoding, Linguistics,
Task analysis, Logic gates, Computer vision,
deep learning
BibRef
Mehrtash, A.,
Wells, W.M.,
Tempany, C.M.,
Abolmaesumi, P.,
Kapur, T.,
Confidence Calibration and Predictive Uncertainty Estimation for Deep
Medical Image Segmentation,
MedImg(39), No. 12, December 2020, pp. 3868-3878.
IEEE DOI
2012
Uncertainty, Image segmentation, Calibration, Estimation,
Biomedical imaging, Artificial neural networks, Bayes methods,
fully convolutional neural networks
BibRef
Luo, A.[Ao],
Yang, F.[Fan],
Li, X.[Xin],
Huang, R.[Rui],
Cheng, H.[Hong],
EKENet:
Efficient knowledge enhanced network for real-time scene parsing,
PR(111), 2021, pp. 107671.
Elsevier DOI
2012
Scene parsing, Real-time method, Deep learning
BibRef
Huang, S.Q.[Shao-Qiong],
Huang, M.X.[Meng-Xing],
Zhang, Y.[Yu],
Chen, J.[Jing],
Bhatti, U.[Uzair],
Medical Image Segmentation Using Deep Learning
with Feature Enhancement,
IET-IPR(14), No. 14, December 2020, pp. 3324-3332.
DOI Link
2012
BibRef
Wang, H.C.,
General Deep Learning Segmentation Process Used In Remote Sensing
Images,
ISPRS20(B2:1289-1296).
DOI Link
2012
BibRef
Li, X.,
Liu, Y.,
Xu, K.,
Zhao, Z.,
Liu, S.,
A Context-Based Network For Referring Image Segmentation,
ICIP20(1436-1440)
IEEE DOI
2011
Image segmentation, Visualization, Linguistics, Feature extraction,
Convolution, Decoding, Referring Image Segmentation,
Dense Convolution
BibRef
Beheshti, N.,
Johnsson, L.,
Squeeze U-Net: A Memory and Energy Efficient Image Segmentation
Network,
WiCV20(1495-1504)
IEEE DOI
2008
Fires, Computational modeling, Kernel, Feature extraction,
Graphics processing units, Memory management
BibRef
Zhu, W.,
Myronenko, A.,
Xu, Z.,
Li, W.,
Roth, H.,
Huang, Y.,
Milletari, F.,
Xu, D.,
NeurReg: Neural Registration and Its Application to Image
Segmentation,
WACV20(3606-3615)
IEEE DOI
2006
Image segmentation, Training, Strain, Estimation, Task analysis,
Image registration, Neural networks
BibRef
Kundu, J.N.,
Rajput, G.S.[G. Singh],
Babu, R.V.,
VRT-Net: Real-Time Scene Parsing via Variable Resolution Transform,
WACV20(2038-2045)
IEEE DOI
2006
Image segmentation, Transforms, Estimation, Real-time systems,
Spatial resolution, Computer architecture
BibRef
Kim, Y.,
Choi, S.,
Lee, H.,
Kim, T.,
Kim, C.,
RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised
Video Object Segmentation,
WACV20(2046-2054)
IEEE DOI
2006
Convolution, Training, Object segmentation, Feature extraction,
Robustness, Image segmentation, Image color analysis
BibRef
Park, H.,
Sjösund, L.L.,
Yoo, Y.,
Monet, N.,
Bang, J.,
Kwak, N.,
SINet: Extreme Lightweight Portrait Segmentation Networks with
Spatial Squeeze Modules and Information Blocking Decoder,
WACV20(2055-2063)
IEEE DOI
2006
Image segmentation, Decoding, Convolution, Task analysis,
Feature extraction, Uncertainty, Computational modeling
BibRef
Wang, W.,
Yu, K.,
Hugonot, J.,
Fua, P.,
Salzmann, M.,
Recurrent U-Net for Resource-Constrained Segmentation,
ICCV19(2142-2151)
IEEE DOI
2004
image segmentation, recurrent neural nets, segmentation methods,
deep networks, standard GPUs, recurrent U-Net architecture, Tensile stress
BibRef
Ding, H.,
Jiang, X.,
Liu, A.Q.,
Thalmann, N.M.,
Wang, G.,
Boundary-Aware Feature Propagation for Scene Segmentation,
ICCV19(6818-6828)
IEEE DOI
2004
feature extraction, graph theory, image segmentation,
learning (artificial intelligence), segment regions,
Convolution
BibRef
Ye, L.W.[Lin-Wei],
Rochan, M.[Mrigank],
Liu, Z.[Zhi],
Wang, Y.[Yang],
Cross-Modal Self-Attention Network for Referring Image Segmentation,
CVPR19(10494-10503).
IEEE DOI
2002
BibRef
Cao, J.[Jiale],
Pang, Y.W.[Yan-Wei],
Li, X.L.[Xue-Long],
Triply Supervised Decoder Networks for Joint Detection and Segmentation,
CVPR19(7384-7393).
IEEE DOI
2002
BibRef
Chen, W.Y.[Wu-Yang],
Jiang, Z.[Ziyu],
Wang, Z.Y.[Zhang-Yang],
Cui, K.[Kexin],
Qian, X.N.[Xiao-Ning],
Collaborative Global-Local Networks for Memory-Efficient Segmentation
of Ultra-High Resolution Images,
CVPR19(8916-8925).
IEEE DOI
2002
BibRef
Durall, R.[Ricard],
Pfreundt, F.J.[Franz-Josef],
Köthe, U.[Ullrich],
Keuper, J.[Janis],
Object Segmentation Using Pixel-Wise Adversarial Loss,
GCPR19(303-316).
Springer DOI
1911
BibRef
Hu, T.[Tao],
Dense In Dense: Training Segmentation from Scratch,
ACCV18(VI:454-470).
Springer DOI
1906
BibRef
Pandey, G.,
Dukkipati, A.,
Learning to Segment With Image-Level Supervision,
WACV19(1856-1865)
IEEE DOI
1904
convolution, image classification, image representation,
image segmentation, learning (artificial intelligence),
Force
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
Marin, D.[Dmitrii],
Tang, M.[Meng],
Ben Ayed, I.[Ismail],
Boykov, Y.[Yuri],
Beyond Gradient Descent for Regularized Segmentation Losses,
CVPR19(10179-10188).
IEEE DOI
2002
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
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
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
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
Cannici, M.[Marco],
Ciccone, M.[Marco],
Romanoni, A.[Andrea],
Matteucci, M.[Matteo],
Attention Mechanisms for Object Recognition With Event-Based Cameras,
WACV19(1127-1136)
IEEE DOI
1904
cameras, image recognition, image sequences, neural nets,
object recognition, object recognition, event-based cameras,
Object recognition
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
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
Porzi, L.[Lorenzo],
Rota Bulo, S.[Samuel],
Colovic, A.[Aleksander],
Kontschieder, P.[Peter],
Seamless Scene Segmentation,
CVPR19(8269-8278).
IEEE DOI
2002
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],
Yańez-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
Neural Networks for Semantic Segmentation .