8.3.4.3 Neural Networks for Segmentation

Chapter Contents (Back)
Neural Networks.
See also Neural Networks for Semantic 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

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

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.Z.[Pei-Zhang], 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.Q.[Zhi-Qing], 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

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

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

Li, X.[Xin], Yang, F.[Fan], Luo, A.[Ao], Jiao, Z.C.[Zhi-Cheng], Cheng, H.[Hong], Liu, Z.C.[Zi-Cheng],
EFRNet: Efficient Feature Reconstructing Network for Real-Time Scene Parsing,
MultMed(24), 2022, pp. 2852-2865.
IEEE DOI 2206
Image reconstruction, Semantics, Encoding, Feature extraction, Convolutional codes, Real-time systems, Task analysis, representation learning BibRef

Li, Z., Kamnitsas, K., Glocker, B.,
Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation,
MedImg(40), No. 3, March 2021, pp. 1065-1077.
IEEE DOI 2103
Image segmentation, Training, Task analysis, Data models, Training data, Lesions, Sensitivity, Overfitting, class imbalance, image segmentation BibRef

Tek, F.B.[F. Boray], Çam, I.[Ilker], Karli, D.[Deniz],
Adaptive convolution kernel for artificial neural networks,
JVCIR(75), 2021, pp. 103015.
Elsevier DOI 2103
Adaptive convolution, Multi-scale convolution, Image classification, Residual networks
See also Refined UNet: UNet-Based Refinement Network for Cloud and Shadow Precise Segmentation. BibRef

Ke, R.H.[Ri-Huan], Bugeau, A.[Aurélie], Papadakis, N.[Nicolas], Kirkland, M.[Mark], Schuetz, P.[Peter], Schönlieb, C.B.[Carola-Bibiane],
Multi-Task Deep Learning for Image Segmentation Using Recursive Approximation Tasks,
IP(30), 2021, pp. 3555-3567.
IEEE DOI 2103
Task analysis, Image segmentation, Annotations, Training, Shape, Deep learning, Semantics, Image segmentation, multi-task learning, deep convolutional networks BibRef

Nalepa, J.[Jakub], Myller, M.[Michal], Cwiek, M.[Marcin], Zak, L.[Lukasz], Lakota, T.[Tomasz], Tulczyjew, L.[Lukasz], Kawulok, M.[Michal],
Towards On-Board Hyperspectral Satellite Image Segmentation: Understanding Robustness of Deep Learning through Simulating Acquisition Conditions,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Li, Y.S.[Yan-Sheng], Shi, T.[Te], Zhang, Y.J.[Yong-Jun], Chen, W.[Wei], Wang, Z.B.[Zhi-Bin], Li, H.[Hao],
Learning Deep Semantic Segmentation Network Under Multiple Weakly-Supervised Constraints for Cross-Domain Remote Sensing Image Semantic Segmentation,
PandRS(175), 2021, pp. 20-33.
Elsevier DOI 2105
Cross-domain remote sensing (RS) image semantic segmentation, Weakly-supervised transfer invariant constraint (WTIC), Dynamic optimization strategy BibRef

Hong, D.F.[Dan-Feng], Yao, J.[Jing], Meng, D.Y.[De-Yu], Xu, Z.B.[Zong-Ben], Chanussot, J.[Jocelyn],
Multimodal GANs: Toward Crossmodal Hyperspectral-Multispectral Image Segmentation,
GeoRS(59), No. 6, June 2021, pp. 5103-5113.
IEEE DOI 2106
Image segmentation, Semantics, Robustness, Training, Feature extraction, Imaging, Classification, single image training BibRef

Zhu, W.T.[Wen-Tao], Myronenko, A.[Andriy], Xu, Z.Y.[Zi-Yue], Li, W.Q.[Wen-Qi], Roth, H.R.[Holger R.], Huang, Y.F.[Yu-Fang], Milletari, F.[Fausto], Xu, D.G.[Da-Guang],
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

Tian, Z.T.[Zhuo-Tao], Zhao, H.S.[Heng-Shuang], Shu, M.[Michelle], Yang, Z.C.[Zhi-Cheng], Li, R.Y.[Rui-Yu], Jia, J.Y.[Jia-Ya],
Prior Guided Feature Enrichment Network for Few-Shot Segmentation,
PAMI(44), No. 2, February 2022, pp. 1050-1065.
IEEE DOI 2201
Semantics, Image segmentation, Object segmentation, Training, Finite element analysis, Adaptation models, Feature extraction, scene understanding BibRef

Zhu, H.M.[Hong-Ming], Tan, R.[Rui], Han, L.T.[Le-Tong], Fan, H.F.[Hong-Fei], Wang, Z.[Zeju], Du, B.[Bowen], Liu, S.C.[Si-Cong], Liu, Q.[Qin],
DSSM: A Deep Neural Network with Spectrum Separable Module for Multi-Spectral Remote Sensing Image Segmentation,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Zhao, Y.C.[Yao-Chi], Liu, S.G.[Shi-Guang], Hu, Z.H.[Zhu-Hua],
Focal learning on stranger for imbalanced image segmentation,
IET-IPR(16), No. 5, 2022, pp. 1305-1323.
DOI Link 2203
Training deep net for segmentation with imbalanced data. BibRef

Khehra, B.S.[Baljit Singh], Singh, A.[Arjan], Hura, G.S.[Gurdeep Singh],
Performance evaluation of Shannon and non-Shannon fuzzy 2-partition entropies for image segmentation using teaching-learning-based optimisation,
IJCVR(12), No. 3, 2022, pp. 250-298.
DOI Link 2205
BibRef

Beeche, C.[Cameron], Singh, J.P.[Jatin P.], Leader, J.K.[Joseph K.], Gezer, N.S.[Naciye S.], Oruwari, A.P.[Amechi P.], Dansingani, K.K.[Kunal K.], Chhablani, J.[Jay], Pu, J.T.[Jian-Tao],
Super U-Net: A modularized generalizable architecture,
PR(128), 2022, pp. 108669.
Elsevier DOI 2205
Image segmentation, U-Net, Dynamic receptive field, Fusion upsampling BibRef

Sun, X.[Xin], Chen, C.R.[Chang-Rui], Wang, X.R.[Xiao-Rui], Dong, J.Y.[Jun-Yu], Zhou, H.Y.[Hui-Yu], Chen, S.[Sheng],
Gaussian Dynamic Convolution for Efficient Single-Image Segmentation,
CirSysVideo(32), No. 5, May 2022, pp. 2937-2948.
IEEE DOI 2205
Image segmentation, Convolution, Task analysis, Semantics, Feature extraction, Training, Kernel, Image segmentation, dynamic receptive field BibRef

Zhang, M.[Miao], Shi, M.J.[Miao-Jing], Li, L.[Li],
MFNet: Multiclass Few-Shot Segmentation Network with Pixel-Wise Metric Learning,
CirSysVideo(32), No. 12, December 2022, pp. 8586-8598.
IEEE DOI 2212
Image segmentation, Feature extraction, Task analysis, Semantics, Prototypes, Measurement, Decoding, Few-shot segmentation, metric learning BibRef

Hernández-Cámara, P.[Pablo], Vila-Tomás, J.[Jorge], Laparra, V.[Valero], Malo, J.[Jesús],
Neural networks with divisive normalization for image segmentation,
PRL(173), 2023, pp. 64-71.
Elsevier DOI 2310
Adaptation, Manifold alignment, Nonlinear interactions, Divisive normalization, Image segmentation, Cityscapes dataset BibRef

Shao, W.X.[Wen-Xuan], Qi, H.[Hao], Dong, X.[Xinghui],
A learnable support selection scheme for boosting few-shot segmentation,
PR(148), 2024, pp. 110202.
Elsevier DOI Code:
WWW Link. 2402
Image segmentation, Few-shot segmentation, Support selection, Few-shot learning, Meta learning BibRef

Zhang, Y.Q.[Yu-Qing], Zhang, Y.[Yong], Piao, X.[Xinglin], Yuan, P.[Peng], Hu, Y.L.[Yong-Li], Yin, B.C.[Bao-Cai],
Cross-modal fusion encoder via graph neural network for referring image segmentation,
IET-IPR(18), No. 4, 2024, pp. 1083-1095.
DOI Link Code:
WWW Link. 2403
image fusion, image segmentation BibRef


Zhang, M.Y.[Min-Ying], Bu, T.P.[Tian-Peng], Hu, L.[Lulu],
A Dynamic Dual-Processing Object Detection Framework Inspired by the Brain's Recognition Mechanism,
ICCV23(6241-6251)
IEEE DOI 2401
BibRef

Zhao, J.[Jing], Sun, L.[Li], Li, Q.L.[Qing-Li],
RecursiveDet: End-to-End Region-based Recursive Object Detection,
ICCV23(6284-6293)
IEEE DOI Code:
WWW Link. 2401
BibRef

Wu, R.H.[Ruo-Hao], Xiao, X.[Xi], Hu, G.W.[Guang-Wu], Zhao, H.Q.[Han-Qing], Zhang, H.[Han], Peng, Y.Q.[Yong-Qing],
Fibonet: A Light-weight and Efficient Neural Network for Image Segmentation,
ICIP23(1345-1349)
IEEE DOI 2312
BibRef

Barrachina, J.A., Ren, C., Vieillard, G., Morisseau, C., Ovarlez, J.P.,
Real- and Complex-Valued Neural Networks for SAR Image Segmentation Through Different Polarimetric Representations,
ICIP22(1456-1460)
IEEE DOI 2211
Databases, Neural networks, Memory management, Apertures, Radar polarimetry, Polarimetric synthetic aperture radar, coherency matrix BibRef

Covell, M.[Michele], Marwood, D.[David], Baluja, S.[Shumeet],
Adding Non-Linear Context to Deep Networks,
ICIP22(671-675)
IEEE DOI 2211
Training, Deep learning, Convolution, Error analysis, Neural networks, Object segmentation, Video compression, ResNet BibRef

Zhang, Y.M.[Yu-Ming], Hsieh, J.W.[Jun-Wei], Lee, C.C.[Chun-Chieh], Fan, K.C.[Kuo-Chin],
SFPN: Synthetic FPN for Object Detection,
ICIP22(1316-1320)
IEEE DOI 2211
Visualization, Fuses, Object detection, Detectors, Feature extraction, Residual neural networks, object detection, multi-scale BibRef

Xiong, Z.T.[Zhi-Tong], Li, H.P.[Hao-Peng], Zhu, X.X.[Xiao Xiang],
Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation,
ECCV22(XX:133-150).
Springer DOI 2211
BibRef

Shi, X.Y.[Xin-Yu], Wei, D.[Dong], Zhang, Y.[Yu], Lu, D.H.[Dong-Huan], Ning, M.[Munan], Chen, J.S.[Jia-Shun], Ma, K.[Kai], Zheng, Y.F.[Ye-Feng],
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation,
ECCV22(XX:151-168).
Springer DOI 2211
BibRef

He, X.Z.[Xing-Zhe], Wandt, B.[Bastian], Rhodin, H.[Helge],
GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation,
CVPR22(1215-1225)
IEEE DOI 2210
Training, Image segmentation, Solid modeling, Image synthesis, Shape, Robustness, Segmentation, grouping and shape analysis, Self- semi- meta- unsupervised learning BibRef

Guo, R.[Ruohao], Niu, D.T.[Dan-Tong], Qu, L.[Liao], Li, Z.B.[Zhen-Bo],
SOTR: Segmenting Objects with Transformers,
ICCV21(7137-7146)
IEEE DOI 2203
Training, Tensors, Pipelines, Neural networks, Detectors, Transformer cores, Transformers, Segmentation, grouping and shape, Recognition and classification BibRef

Wang, J.[Jin], Zhang, B.F.[Bing-Feng], Liu, W.F.[Wei-Feng], Liu, B.[Baodi], Yu, S.Y.[Si-Yue],
Self-Compensating Learning for Few-Shot Segmentation,
ICIP23(1320-1324)
IEEE DOI 2312
BibRef

Zhang, B.F.[Bing-Feng], Xiao, J.[Jimin], Qin, T.[Terry],
Self-Guided and Cross-Guided Learning for Few-Shot Segmentation,
CVPR21(8308-8317)
IEEE DOI 2111
Support vector machines, Image segmentation, Codes, Pipelines, Data mining BibRef

Li, G.[Gen], Jampani, V.[Varun], Sevilla-Lara, L.[Laura], Sun, D.[Deqing], Kim, J.H.[Jong-Hyun], Kim, J.[Joongkyu],
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation,
CVPR21(8330-8339)
IEEE DOI 2111
Image segmentation, Adaptation models, Shape, Computational modeling, Prototypes, Feature extraction BibRef

Boudiaf, M.[Malik], Kervadec, H.[Hoel], Masud, Z.I.[Ziko Imtiaz], Piantanida, P.[Pablo], Ben Ayed, I.[Ismail], Dolz, J.[Jose],
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?,
CVPR21(13974-13983)
IEEE DOI 2111
Training, Image segmentation, Codes, Benchmark testing, Feature extraction, Entropy BibRef

Pham, D.D.[Duc Duy], Dovletov, G.[Gurbandurdy], Pauli, J.[Josef],
A Differentiable Convolutional Distance Transform Layer for Improved Image Segmentation,
GCPR20(432-444).
Springer DOI 2110
BibRef

Burrows, L.[Liam], Chen, K.[Ke], Torella, F.[Francesco],
A Deep Image Prior Learning Algorithm for Joint Selective Segmentation and Registration,
SSVM21(411-422).
Springer DOI 2106
BibRef

Raipuria, G.[Geetank], Bonthu, S.[Saikiran], Singhal, N.[Nitin],
Noise Robust Training of Segmentation Model Using Knowledge Distillation,
AIDP20(97-104).
Springer DOI 2103
BibRef

Wang, H.C.,
General Deep Learning Segmentation Process Used In Remote Sensing Images,
ISPRS20(B2:1289-1296).
DOI Link 2012
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

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., Magnenat-Thalmann, N.[Nadia], 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

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

Pi, P.C.[Peng-Cheng], Jiang, Z.[Ziyu], Xiong, Z.X.[Zi-Xiang],
Learning Skip Map for Efficient Ultra-High Resolution Image Segmentation,
ICIP21(634-638)
IEEE DOI 2201
Image segmentation, Image resolution, Computational complexity, Image segmentation, ultra-high resolution images, skip map 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

Marin, D.[Dmitrii], Tang, M.[Meng], Ben Ayed, I.[Ismail], Boykov, Y.Y.[Yuri Y.],
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.Y.[Yuri Y.],
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

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

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 .


Last update:May 29, 2024 at 17:34:46