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Aggregates
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Image segmentation, Computer architecture, Convolution, Training,
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Feature extraction, Object segmentation, Visualization,
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2002
Fasteners, Risk management, Optimization, Training,
Complexity theory, Task analysis, Indexes, Lovász extension,
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2002
Image segmentation, Biomedical imaging, Training,
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Image segmentation, Feature fusion, Bipartite graph, Spectral clustering
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Infrared image segmentation, Pulse coupled neural network,
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BibRef
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2010
U-Net, Medical image segmentation,
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2011
Image segmentation, Visualization, Decoding, Linguistics,
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IEEE DOI
2002
BibRef
Mehrtash, A.,
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IEEE DOI
2012
Uncertainty, Image segmentation, Calibration, Estimation,
Biomedical imaging, Artificial neural networks, Bayes methods,
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2012
Scene parsing, Real-time method, Deep learning
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Huang, S.Q.[Shao-Qiong],
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2103
Code, Segmentation.
WWW Link. Convolutional neural network, Non-elongated tissue,
Crossover-Net, Image segmentation, Crossover-patch
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Glocker, B.,
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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
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Elsevier DOI
2103
Adaptive convolution, Multi-scale convolution,
Image classification, Residual networks
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Multi-Task Deep Learning for Image Segmentation Using Recursive
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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
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Nalepa, J.[Jakub],
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Tulczyjew, L.[Lukasz],
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Towards On-Board Hyperspectral Satellite Image Segmentation:
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Acquisition Conditions,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Li, Y.S.[Yan-Sheng],
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Wang, Z.B.[Zhi-Bin],
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Learning Deep Semantic Segmentation Network Under Multiple
Weakly-Supervised Constraints for Cross-Domain Remote Sensing Image
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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],
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Meng, D.Y.[De-Yu],
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Multimodal GANs:
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GeoRS(59), No. 6, June 2021, pp. 5103-5113.
IEEE DOI
2106
Image segmentation, Semantics, Robustness, Training,
Feature extraction, Imaging, Classification,
single image training
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Nath, V.[Vishwesh],
Yang, D.[Dong],
Landman, B.A.[Bennett A.],
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Roth, H.R.[Holger R.],
Diminishing Uncertainty Within the Training Pool:
Active Learning for Medical Image Segmentation,
MedImg(40), No. 10, October 2021, pp. 2534-2547.
IEEE DOI
2110
Image segmentation, Biomedical imaging, Deep learning, Training,
Task analysis, Mutual information, Hippocampus, Deep Learning,
SVGD
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
Feng, R.[Ruiwei],
Zheng, X.S.[Xiang-Shang],
Gao, T.X.[Tian-Xiang],
Chen, J.[Jintai],
Wang, W.Z.[Wen-Zhe],
Chen, D.Z.[Danny Z.],
Wu, J.[Jian],
Interactive Few-Shot Learning:
Limited Supervision, Better Medical Image Segmentation,
MedImg(40), No. 10, October 2021, pp. 2575-2588.
IEEE DOI
2110
Image segmentation, Task analysis, Biomedical imaging, Training,
Annotations, Deep learning, Optimization, limited supervision
BibRef
Cui, H.J.[Heng-Ji],
Wei, D.[Dong],
Ma, K.[Kai],
Gu, S.[Shi],
Zheng, Y.F.[Ye-Feng],
A Unified Framework for Generalized Low-Shot Medical Image
Segmentation With Scarce Data,
MedImg(40), No. 10, October 2021, pp. 2656-2671.
IEEE DOI
2110
Image segmentation, Biomedical imaging, Training, Annotations,
Task analysis, Diseases, adaptive mixing coefficients
BibRef
Wang, L.[Lu],
Guo, D.[Dong],
Wang, G.[Guotai],
Zhang, S.T.[Shao-Ting],
Annotation-Efficient Learning for Medical Image Segmentation Based on
Noisy Pseudo Labels and Adversarial Learning,
MedImg(40), No. 10, October 2021, pp. 2795-2807.
IEEE DOI
2110
Image segmentation, Training, Annotations, Shape, Biomedical imaging,
Noise measurement, Deep learning, Segmentation, deep learning,
noisy labels
BibRef
Tian, Z.T.[Zhuo-Tao],
Zhao, H.S.[Heng-Shuang],
Shu, M.[Michelle],
Yang, Z.C.[Zhi-Cheng],
Li, R.[Ruiyu],
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
Qu, L.[Lei],
Wang, M.[Meng],
Guo, K.X.[Kai-Xuan],
Wan, W.[Wan],
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Tang, J.[Jun],
Wu, J.[Jun],
Duan, P.[Peng],
Biomedical image segmentation based on full-Resolution network,
PRL(153), 2022, pp. 232-238.
Elsevier DOI
2201
Image Segmentation, Biomedical Image, Full-resolution,
Convolutional Neural Network
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.[Jonghyun],
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
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
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
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
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Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Neural Networks for Semantic Segmentation .