6.1.2.1 Edge Detectors Based on Learning, Neural Nets, etc.

Lu, S., Szeto, A.,
Improving Edge Measurement on Noisy Images by Hierarchical Neural Networks,
PRL(12), 1991, pp. 155-164. BibRef 9100

Lu, S.W.[Si-Wei], Szeto, A.[Anthony],
Hierarchical Artificial Neural Networks for Edge Enhancement,
PR(26), No. 8, August 1993, pp. 1149-1163.
Elsevier DOI BibRef 9308

Srinivasan, V., Bhatia, P., Ong, S.H.,
Edge-Detection Using a Neural-Network,
PR(27), No. 12, December 1994, pp. 1653-1662.
Elsevier DOI BibRef 9412

Wong, H.S.[Hau-San], Caelli, T.M.[Terry M.], Guan, L.[Ling],
A model-based neural network for edge characterization,
PR(33), No. 3, March 2000, pp. 427-444.
Elsevier DOI 0001
BibRef

Suzuki, K.[Kenji], Horiba, I.[Isao], Sugie, N.[Noboru],
Neural edge enhancer for supervised edge enhancement from noisy images,
PAMI(25), No. 12, December 2003, pp. 1582-1596.
IEEE Abstract. 0401
Apply NN learning to edges. BibRef

Lu, S.W.[Si-Wei], Wang, Z.Q.[Zi-Qing], Shen, J.[Jun],
Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement,
PR(36No. 10, October 2003, pp. 2395-2409.
Elsevier DOI 0308
Neural net for edges. BibRef

González Velasco, H.M.[Horacio M.], García Orellana, C.J.[Carlos J.], Macías Macías, M.[Miguel], López Aligué, F.J.[F. Javier], Acevedo Sotoca, M.I.[M. Isabel],
Neural-networks-based edges selector for boundary extraction problems,
IVC(22), No. 13, 1 November 2004, pp. 1129-1135.
Elsevier DOI 0410
Remove background edges before generating the boundary representation. BibRef

Farabet, C.[Clement], Couprie, C.[Camille], Najman, L.[Laurent], Le Cun, Y.L.[Yann L.],
Learning Hierarchical Features for Scene Labeling,
PAMI(35), No. 8, 2013, pp. 1915-1929.
IEEE DOI 1307
Image edge detection; Convolutional networks; deep learning; scene parsing BibRef

Pan, B.[Bin], Shi, Z.W.[Zhen-Wei], Xu, X.[Xia],
Hierarchical Guidance Filtering-Based Ensemble Classification for Hyperspectral Images,
GeoRS(55), No. 7, July 2017, pp. 4177-4189.
IEEE DOI 1706
Data mining, Feature extraction, Hyperspectral imaging, Image edge detection, Support vector machines, Training, Ensemble learning, hierarchical guidance filtering (HGF), hyperspectral, image, (HSI), classification BibRef

Stevens, J.R., Resmini, R.G., Messinger, D.W.,
Spectral-Density-Based Graph Construction Techniques for Hyperspectral Image Analysis,
GeoRS(55), No. 10, October 2017, pp. 5966-5983.
IEEE DOI 1710
data mining, edge detection, graph theory, hyperspectral imaging, remote sensing, HSI, data mining, density-based edge allocation, density-weighted graph construction, derived manifold coordinates, graph theory, BibRef

Kang, X.D.[Xu-Dong], Xiang, X., Li, S.T.[Shu-Tao], Benediktsson, J.A.[Jón Atli],
PCA-Based Edge-Preserving Features for Hyperspectral Image Classification,
GeoRS(55), No. 12, December 2017, pp. 7140-7151.
IEEE DOI 1712
Feature extraction, Hyperspectral imaging, Image edge detection, Principal component analysis, Support vector machines, support vector machine (SVM) BibRef

Cui, B.[Binge], Xie, X.Y.[Xiao-Yun], Ma, X.D.[Xiu-Dan], Ren, G.B.[Guang-Bo], Ma, Y.[Yi],
Superpixel-Based Extended Random Walker for Hyperspectral Image Classification,
GeoRS(56), No. 6, June 2018, pp. 3233-3243.
IEEE DOI 1806
Hyperspectral imaging, Image edge detection, Image segmentation, Kernel, Shape, Support vector machines, weighted graph BibRef

Cui, B.[Binge], Xie, X.Y.[Xiao-Yun], Hao, S.Y.[Si-Yuan], Cui, J.[Jiandi], Lu, Y.[Yan],
Semi-Supervised Classification of Hyperspectral Images Based on Extended Label Propagation and Rolling Guidance Filtering,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Wen, C.B.[Chang-Bao], Liu, P.L.[Peng-Li], Ma, W.B.[Wen-Bo], Jian, Z.R.[Zhi-Rong], Lv, C.H.[Chang-Heng], Hong, J.T.[Ji-Tong], Shi, X.W.[Xiao-Wen],
Edge detection with feature re-extraction deep convolutional neural network,
JVCIR(57), 2018, pp. 84-90.
Elsevier DOI 1812
Edge detection, Feature re-extract, Deep convolutional neural network, Generalization ability BibRef

Xie, S.N.[Sai-Ning], Tu, Z.W.[Zhuo-Wen],
Holistically-Nested Edge Detection,
IJCV(125), No. 1-3, December 2018, pp. 3-18.
Springer DOI 1711
BibRef
Earlier: ICCV15(1395-1403)
IEEE DOI 1602
Award, Marr Prize, HM. Detectors. multi-scale. Edges from a neural model. Holistic training and multi-level learning.
See also Brief Analysis of the Holistically-Nested Edge Detector, A. BibRef

He, Y., Ni, L.M.,
A Novel Scheme Based on the Diffusion to Edge Detection,
IP(28), No. 4, April 2019, pp. 1613-1624.
IEEE DOI 1901
computational complexity, edge detection, learning (artificial intelligence), neural nets, edge detection, Bessel potential BibRef

Andrade-Loarca, H.[Hector], Kutyniok, G.[Gitta], Öktem, O.[Ozan], Petersen, P.C.[Philipp C.],
Extraction of Digital Wavefront Sets Using Applied Harmonic Analysis and Deep Neural Networks,
SIIMS(12), No. 4, 2019, pp. 1936-1966.
DOI Link 1912
BibRef

Duan, P., Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Ghamisi, P., Benediktsson, J.A.[Jón Atli],
Fusion of Multiple Edge-Preserving Operations for Hyperspectral Image Classification,
GeoRS(57), No. 12, December 2019, pp. 10336-10349.
IEEE DOI 1912
Image edge detection, Smoothing methods, Feature extraction, Support vector machines, Transforms, Hyperspectral imaging, image classification BibRef

Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., Bourgeat, P.,
Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis,
MedImg(38), No. 7, July 2019, pp. 1750-1762.
IEEE DOI 1907
Image edge detection, Image generation, Generative adversarial networks, Generators, Imaging, brain BibRef

Zhu, F.D.[Fei-Da], Fang, C.W.[Chao-Wei], Ma, K.K.[Kai-Kuang],
PNEN: Pyramid Non-Local Enhanced Networks,
IP(29), 2020, pp. 8831-8841.
IEEE DOI 2009
Image resolution, Correlation, Task analysis, Smoothing methods, Neural networks, Image edge detection, Image restoration, deep convolutional neural networks BibRef

Fan, Q.N.[Qing-Nan], Chen, D.D.[Dong-Dong], Yuan, L.[Lu], Hua, G.[Gang], Yu, N.H.[Neng-Hai], Chen, B.Q.[Bao-Quan],
A General Decoupled Learning Framework for Parameterized Image Operators,
PAMI(43), No. 1, January 2021, pp. 33-47.
IEEE DOI 2012
Convolution, Task analysis, Image resolution, Acceleration, Image edge detection, Runtime, Fans, smoothing BibRef

Xiong, C.[Chao], Li, W.[Wen], Liu, Y.[Yun], Wang, M.H.[Ming-Hui],
Multi-Dimensional Edge Features Graph Neural Network on Few-Shot Image Classification,
SPLetters(28), 2021, pp. 573-577.
IEEE DOI 2104
Training, Task analysis, Feature extraction, Image edge detection, Convolution, Graph neural networks, Benchmark testing, image classification BibRef

Feng, Z.X.[Zhi-Xi], Yang, S.Y.[Shu-Yuan], Wang, M.[Min], Jiao, L.C.[Li-Chen],
Learning Dual Geometric Low-Rank Structure for Semisupervised Hyperspectral Image Classification,
Cyber(51), No. 1, January 2021, pp. 346-358.
IEEE DOI 2012
Hyperspectral imaging, Laplace equations, Training, Image edge detection, Support vector machines, Cybernetics, support vector machine BibRef

Song, L.L.[Liang-Liang], Feng, Z.X.[Zhi-Xi], Yang, S.Y.[Shu-Yuan], Zhang, X.Y.[Xin-Yu], Jiao, L.C.[Li-Cheng],
Self-Supervised Assisted Semi-Supervised Residual Network for Hyperspectral Image Classification,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Wan, S.[Sheng], Gong, C.[Chen], Zhong, P.[Ping], Du, B.[Bo], Zhang, L.F.[Le-Fei], Yang, J.[Jian],
Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification,
GeoRS(58), No. 5, May 2020, pp. 3162-3177.
IEEE DOI 2005
Hyperspectral imaging, Convolution, Feature extraction, Kernel, Support vector machines, Training, Dynamic graph, multiscale information BibRef

Wan, S.[Sheng], Gong, C.[Chen], Zhong, P.[Ping], Pan, S.R.[Shi-Rui], Li, G.Y.[Guang-Yu], Yang, J.[Jian],
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network,
GeoRS(59), No. 1, January 2021, pp. 597-612.
IEEE DOI 2012
Image edge detection, Feature extraction, Hyperspectral imaging, Nonhomogeneous media, Data mining, hyperspectral image~(HIS) classification BibRef

Wan, S.[Sheng], Pan, S.R.[Shi-Rui], Zhong, S.W.[Sheng-Wei], Yang, J.[Jie], Yang, J.[Jian], Zhan, Y.B.[Yi-Bing], Gong, C.[Chen],
Multi-level graph learning network for hyperspectral image classification,
PR(129), 2022, pp. 108705.
Elsevier DOI 2206
Graph convolutional network, Graph-based machine learning, Hyperspectral image classification, Remote sensing, Graph structural learning BibRef

Liu, C.G.[Chen-Guang], Tupin, F.[Florence], Gousseau, Y.[Yann],
Training CNNs on speckled optical dataset for edge detection in SAR images,
PandRS(170), 2020, pp. 88-102.
Elsevier DOI 2011
Edge detection, 1-look SAR image, Optical dataset, CNNs, Hand-crafted layer, GRHED BibRef

Suzuki, S.[Satoshi], Takeda, S.[Shoichiro], Takagi, M.[Motohiro], Tanida, R.[Ryuichi], Kimata, H.[Hideaki], Shouno, H.[Hayaru],
Deep Feature Compression Using Spatio-Temporal Arrangement Toward Collaborative Intelligent World,
CirSysVideo(32), No. 6, June 2022, pp. 3934-3946.
IEEE DOI 2206
BibRef
Earlier: A1, A3, A2, A4, A5, Only:
Deep Feature Compression With Spatio-Temporal Arranging for Collaborative Intelligence,
ICIP20(3099-3103)
IEEE DOI 2011
Image coding, Correlation, Image edge detection, Video compression, Cloud computing, Quantization (signal), ordering search algorithm. Collaborative intelligence, spatio-temporal arranging BibRef

Grompone von Gioi, R.[Rafael], Randall, G.[Gregory],
A Brief Analysis of the Holistically-Nested Edge Detector,
IPOL(12), 2022, pp. 369-377.
DOI Link 2210

See also Holistically-Nested Edge Detection. BibRef

Grompone von Gioi, R.[Rafael], Randall, G.[Gregory],
A Brief Analysis of the Dense Extreme Inception Network for Edge Detection,
IPOL(12), 2022, pp. 389-403.
DOI Link 2210

See also Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection. BibRef

Elharrouss, O.[Omar], Hmamouche, Y.[Youssef], Idrissi, A.K.[Assia Kamal], El Khamlichi, B.[Btissam], El Fallah-Seghrouchni, A.[Amal],
Refined edge detection with cascaded and high-resolution convolutional network,
PR(138), 2023, pp. 109361.
Elsevier DOI 2303
Edge detection, Convolutional neural networks, Deep learning, Scale-representation, Backbone BibRef

Soria, X.[Xavier], Sappa, A.[Angel], Humanante, P.[Patricio], Akbarinia, A.[Arash],
Dense extreme inception network for edge detection,
PR(139), 2023, pp. 109461.
Elsevier DOI 2304
Edge detection, Deep learning, CNN, Contour detection, Boundary detection, Segmentation BibRef

Xian, R.H.[Rong-Hao], Xiong, X.[Xin], Peng, H.[Hong], Wang, J.[Jun], de Arellano Marrero, A.R.[Antonio Ramírez], Yang, Q.[Qian],
Feature fusion method based on spiking neural convolutional network for edge detection,
PR(147), 2024, pp. 110112.
Elsevier DOI Code:
WWW Link. 2312
Edge detection, Feature fusion, Nonlinear spiking neural P systems, NSNP-type neuron model BibRef

Zhang, X.[Xiao], Lin, C.[Chuan], Li, F.Z.[Fu-Zhang], Cao, Y.J.[Yi-Jun], Li, Y.J.[Yong-Jie],
LVP-net: A deep network of learning visual pathway for edge detection,
IVC(147), 2024, pp. 105078.
Elsevier DOI 2406
Edge detection, Enhancer, Color-opponency, Visual pathway, Convolutional neural networks BibRef

Wibisono, J.K., Hang, H.M.,
Traditional Method Inspired Deep Neural Network For Edge Detection,
ICIP20(678-682)
IEEE DOI 2011
Image edge detection, Feature extraction, Detectors, Neural networks, Complexity theory, Training, Machine learning, CNN BibRef

Soria, X., Riba, E., Sappa, A.D.[Angel D.],
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection,
WACV20(1912-1921)
IEEE DOI 2006
Image edge detection, Convolution, Training, Feeds, Machine learning, Task analysis, Kernel
See also Brief Analysis of the Dense Extreme Inception Network for Edge Detection, A. BibRef

Jung, J.H.[Jay Hoon], Kwon, Y.M.[Young-Min],
Color, Edge, and Pixel-wise Explanation of Predictions Based on Interpretable Neural Network Model,
ICPR21(6003-6010)
IEEE DOI 2105
Image color analysis, Shape, Image edge detection, Neural networks, Predictive models, Tools, Deep Neural Network BibRef

Iwai, S.[Shoma], Miyazaki, T.[Tomo], Sugaya, Y.[Yoshihiro], Omachi, S.[Shinichiro],
Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks,
ICPR21(8235-8242)
IEEE DOI 2105
Training, Interpolation, Image coding, Image edge detection, Bit rate, Generative adversarial networks, Entropy BibRef

Matsubara, Y.[Yoshitomo], Levorato, M.[Marco],
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks,
ICPR21(2272-2279)
IEEE DOI 2105
Support the computation. Image edge detection, Computational modeling, Neural networks, Object detection, split computing. BibRef

Zheng, W., Gou, C., Yan, L., Wang, F.,
Differential-Evolution-Based Generative Adversarial Networks for Edge Detection,
CEFRL19(2999-3008)
IEEE DOI 2004
edge detection, evolutionary computation, learning (artificial intelligence), neural nets, Edge detection BibRef

Zhao, B.[Bo], Sun, X.W.[Xin-Wei], Hong, X.P.[Xiao-Peng], Yao, Y.[Yuan], Wang, Y.Z.[Yi-Zhou],
Zero-Shot Learning Via Recurrent Knowledge Transfer,
WACV19(1308-1317)
IEEE DOI 1904
graph theory, learning (artificial intelligence), object recognition, pattern clustering, learned SSS, Image edge detection BibRef

Douze, M., Szlam, A., Hariharan, B., Jégou, H.,
Low-Shot Learning with Large-Scale Diffusion,
CVPR18(3349-3358)
IEEE DOI 1812
Sparse matrices, Semisupervised learning, Visualization, Diffusion processes, Training, Measurement, Image edge detection BibRef

Lee, J., Zaheer, M.Z., Astrid, M., Lee, S.,
SmoothMix: a Simple Yet Effective Data Augmentation to Train Robust Classifiers,
DeepVision20(3264-3274)
IEEE DOI 2008
Training, Image edge detection, Robustness, Predictive models, Kernel, Task analysis, Transforms BibRef

Croce, F., Hein, M.,
Sparse and Imperceivable Adversarial Attacks,
ICCV19(4723-4731)
IEEE DOI 2004
gradient methods, learning (artificial intelligence), neural nets, pattern classification, security of data, Image edge detection BibRef

Che, F., Zhu, X., Yang, T., Yu, T.,
3SGAN: 3D Shape Embedded Generative Adversarial Networks,
AIM19(3305-3314)
IEEE DOI 2004
edge detection, image colour analysis, learning (artificial intelligence), neural nets, multiview BibRef

Prabhu, A.[Ameya], Batchu, V.[Vishal], Munagala, S.A.[Sri Aurobindo], Gajawada, R.[Rohit], Namboodiri, A.[Anoop],
Distribution-Aware Binarization of Neural Networks for Sketch Recognition,
WACV18(830-838)
IEEE DOI 1806
data compression, edge detection, image coding, learning (artificial intelligence), neural nets, Task analysis BibRef

Zhang, Q., Zhang, M., Wang, M., Sui, W., Meng, C., Yang, J., Kong, W., Cui, X., Lin, W.,
Efficient Deep Learning Inference Based on Model Compression,
EfficientDeep18(1776-17767)
IEEE DOI 1812
Computational modeling, Convolution, Adaptation models, Image edge detection, Quantization (signal), Kernel BibRef

Chen, W., Hays, J.,
SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis,
CVPR18(9416-9425)
IEEE DOI 1812
Image edge detection, Image generation, Training, Databases, Task analysis, Generative adversarial networks BibRef

Chen, Y., Lai, Y., Liu, Y.,
CartoonGAN: Generative Adversarial Networks for Photo Cartoonization,
CVPR18(9465-9474)
IEEE DOI 1812
Training, Generative adversarial networks, Manifolds, Image edge detection, Automobiles, Training data BibRef

Shahin Shamsabadi, A., Haddadi, H., Cavallaro, A.,
Distributed One-Class Learning,
ICIP18(4123-4127)
IEEE DOI 1809
Training, Image reconstruction, Privacy, Training data, Feature extraction, Image edge detection, Data privacy, Privacy BibRef

Etemad, E., Gao, Q.,
Object localization by optimizing convolutional neural network detection score using generic edge features,
ICIP17(675-679)
IEEE DOI 1803
Convolutional neural networks, Image edge detection, Object recognition, Optimization, Proposals, Search problems, RCNN BibRef

Yu, L., Fan, G.,
Edge-aware integration model for semantic labeling of rare classes,
ICIP17(4482-4486)
IEEE DOI 1803
Image color analysis, Image edge detection, Image segmentation, Labeling, Probabilistic logic, Semantics, Training, CNN, Superpixel BibRef

Tamaazousti, Y.[Youssef], Le Borgne, H.[Hervé], Hudelot, C.[Céline],
MuCaLe-Net: Multi Categorical-Level Networks to Generate More Discriminating Features,
CVPR17(5282-5291)
IEEE DOI 1711
Additives, Image edge detection, Image representation, Proposals, Psychology, Standards BibRef

Danilo, R., Wouafo, H.N., Chavet, C., Gripon, V., Conde-Canencia, L., Coussy, P.,
Associative Memory based on clustered Neural Networks: Improved model and architecture for Oriented Edge Detection,
DASIP16(51-58)
IEEE DOI 1704
content-addressable storage BibRef

Ganin, Y.[Yaroslav], Lempitsky, V.[Victor],
N4-Fields: Neural Network Nearest Neighbor Fields for Image Transforms,
ACCV14(II: 536-551).
Springer DOI 1504
for edges or thin structures. BibRef

Chen, J.S.[Jian-Sheng], He, J.P.[Jin-Ping], Su, G.D.[Guang-Da],
Combining image entropy with the Pulse Coupled Neural Network in edge detection,
ICIP10(1637-1640).
IEEE DOI 1009
BibRef

Wang, J.L.[Jian-Lai], Yang, C.L.[Chun-Ling], Sun, C.[Chao],
A Novel Algorithm for Edge Detection of Remote Sensing Image Based on CNN and PSO,
CISP09(1-5).
IEEE DOI 0910
BibRef

Li, J.J.[Jian-Jun], Wei, Z.H.[Zhi-Hui], Zhang, Z.J.[Zheng-Jun], Xie, J.C.[Jian-Chun],
Edge Detection Method Based on Multi-Expert Information Fusion,
CISP09(1-4).
IEEE DOI 0910
BibRef

Chapter on Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform continues in
Basic Edges, General Discussion, Analysis .