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Fasteners, Risk management, Optimization, 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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Scene parsing, Real-time method, Deep learning
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Image reconstruction, Semantics, Encoding, Feature extraction,
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Image segmentation, Training, Task analysis, Data models,
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Adaptive convolution, Multi-scale convolution,
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2103
Task analysis, Image segmentation, Annotations, Training, Shape,
Deep learning, Semantics, Image segmentation, multi-task learning,
deep convolutional networks
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Towards On-Board Hyperspectral Satellite Image Segmentation:
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Learning Deep Semantic Segmentation Network Under Multiple
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2105
Cross-domain remote sensing (RS) image semantic segmentation,
Weakly-supervised transfer invariant constraint (WTIC),
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Multimodal GANs:
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IEEE DOI
2106
Image segmentation, Semantics, Robustness, Training,
Feature extraction, Imaging, Classification,
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Zhu, W.T.[Wen-Tao],
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Xu, Z.Y.[Zi-Yue],
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NeurReg: Neural Registration and Its Application to Image
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WACV20(3606-3615)
IEEE DOI
2006
Image segmentation, Training, Strain, Estimation, Task analysis,
Image registration, Neural networks
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Tian, Z.T.[Zhuo-Tao],
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Prior Guided Feature Enrichment Network for Few-Shot Segmentation,
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IEEE DOI
2201
Semantics, Image segmentation, Object segmentation, Training,
Finite element analysis, Adaptation models, Feature extraction,
scene understanding
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Zhu, H.M.[Hong-Ming],
Tan, R.[Rui],
Han, L.T.[Le-Tong],
Fan, H.F.[Hong-Fei],
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Zhao, Y.C.[Yao-Chi],
Liu, S.G.[Shi-Guang],
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IET-IPR(16), No. 5, 2022, pp. 1305-1323.
DOI Link
2203
Training deep net for segmentation with imbalanced data.
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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
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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
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Zhang, M.[Miao],
Shi, M.J.[Miao-Jing],
Li, L.[Li],
MFNet: Multiclass Few-Shot Segmentation Network with Pixel-Wise
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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,
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Elsevier DOI
2310
Adaptation, Manifold alignment, Nonlinear interactions,
Divisive normalization, Image segmentation, Cityscapes dataset
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Shao, W.X.[Wen-Xuan],
Qi, H.[Hao],
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A learnable support selection scheme for boosting few-shot
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WWW Link.
2402
Image segmentation, Few-shot segmentation, Support selection,
Few-shot learning, Meta learning
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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
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WWW Link.
2403
image fusion, image segmentation
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Qi, H.[Hao],
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Dong, X.H.[Xing-Hui],
Small Sample Image Segmentation by Coupling Convolutions and
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CirSysVideo(34), No. 7, July 2024, pp. 5282-5294.
IEEE DOI Code:
WWW Link.
2407
Transformers, Image segmentation, Feature extraction,
Task analysis, Couplings, Convolutional neural networks,
cross-attention
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Variyar, V.V.S.[V.V. Sajith],
Sowmya, V.,
Sivanpillai, R.[Ramesh],
Brown, G.K.[Gregory K.],
A multi-branch dual attention segmentation network for epiphyte drone
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Elsevier DOI
2407
UAV image segmentation, Multi-branch network, Dual attention,
Low samples, Mixed quality
BibRef
Bi, H.B.[Hong-Bo],
Tong, Y.[Yuyu],
Zhang, P.[Pan],
Zhang, J.Y.[Jia-Yuan],
Zhang, C.[Cong],
Dual cross-enhancement network for highly accurate dichotomous image
segmentation,
CVIU(248), 2024, pp. 104122.
Elsevier DOI
2409
Deep Learning, Dichotomous Image Segmentation,
Cross-scaling Guidance, Semantic Cross-Transplantation
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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
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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.Y.[Zi-Yu],
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.Y.[Zi-Yu],
Wang, Z.Y.[Zhang-Yang],
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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).
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1810
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Oudni, L.,
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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,
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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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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).
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1608
BibRef
Pathak, D.,
Krahenbuhl, P.,
Darrell, T.J.,
Constrained Convolutional Neural Networks for Weakly Supervised
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ICCV15(1796-1804)
IEEE DOI
1602
Convolutional codes
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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
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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).
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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
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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
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9700
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Neural Networks for Semantic Segmentation .