4.8.2.1 Perceptual Grouping, Saliency, Neural Networks, Learning

Chapter Contents (Back)
Grouping, Perceptual. Perceptual Grouping. Neural Networks. Learning.

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CVPR15(1884-1892)
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Stefic, D.[Daria], Patras, I.[Ioannis],
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IVC(52), No. 1, 2016, pp. 195-205.
Elsevier DOI 1609
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Learning visual saliency using topographic independent component analysis,
ICIP14(1130-1134)
IEEE DOI 1502
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Huang, F., Qi, J., Lu, H., Zhang, L., Ruan, X.,
Salient Object Detection via Multiple Instance Learning,
IP(26), No. 4, April 2017, pp. 1911-1922.
IEEE DOI 1704
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Zhang, L.[Lihe], Ai, J., Jiang, B.[Bowen], Lu, H.C.[Hu-Chuan], Li, X.,
Saliency Detection via Absorbing Markov Chain With Learnt Transition Probability,
IP(27), No. 2, February 2018, pp. 987-998.
IEEE DOI 1712
Computational modeling, Feature extraction, Image segmentation, Markov processes, Object detection, Sparse matrices, transition probability matrix BibRef

Jiang, B.[Bowen], Zhang, L.[Lihe], Lu, H.C.[Hu-Chuan], Yang, C.[Chuan], Yang, M.H.[Ming-Hsuan],
Saliency Detection via Absorbing Markov Chain,
ICCV13(1665-1672)
IEEE DOI 1403
absorbing Markov chain; object detection; saliency detection BibRef

Yang, C.[Chuan], Zhang, L.[Lihe], Lu, H.C.[Hu-Chuan], Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Saliency Detection via Graph-Based Manifold Ranking,
CVPR13(3166-3173)
IEEE DOI 1309
BibRef

Zhang, C.[Chao], Li, X.[Xiong], Ruan, X.[Xiang], Zhao, Y.M.[Yu-Ming], Yang, M.H.[Ming-Hsuan],
Discriminative Generative Contour Detection,
BMVC13(xx-yy).
DOI Link 1402
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Contour detection via random forest,
ICPR12(2772-2775).
WWW Link. 1302
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Li, S.[Shuang], Lu, H.C.[Hu-Chuan], Lin, Z.[Zhe], Shen, X.H.[Xiao-Hui], Price, B.,
Adaptive Metric Learning for Saliency Detection,
IP(24), No. 11, November 2015, pp. 3321-3331.
IEEE DOI 1509
image coding BibRef

Deutsch, S.[Shay], Medioni, G.[Gérard],
Learning the Geometric Structure of Manifolds with Singularities Using the Tensor Voting Graph,
JMIV(57), No. 3, March 2017, pp. 402-422.
WWW Link. 1702
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Earlier:
Unsupervised Learning Using the Tensor Voting Graph,
SSVM15(282-293).
Springer DOI 1506
BibRef

Fu, K.[Keren], Gu, I.Y.H.[Irene Yu-Hua], Yang, J.[Jie],
Saliency Detection by Fully Learning a Continuous Conditional Random Field,
MultMed(19), No. 7, July 2017, pp. 1531-1544.
IEEE DOI 1706
Boosting, Estimation, Feature extraction, Image segmentation, Labeling, Object detection, Continuous conditional random field (C-CRF), feature integration, learning, saliency map, salient object detection, spatial, ranges BibRef

Zeng, Y., Feng, M., Lu, H., Yang, G., Borji, A.,
An Unsupervised Game-Theoretic Approach to Saliency Detection,
IP(27), No. 9, September 2018, pp. 4545-4554.
IEEE DOI 1807
game theory, learning (artificial intelligence), object detection, Saliency Game, complementary features, visual attention BibRef

Murabito, F.[Francesca], Spampinato, C.[Concetto], Palazzo, S.[Simone], Giordano, D.[Daniela], Pogorelov, K.[Konstantin], Riegler, M.[Michael],
Top-down saliency detection driven by visual classification,
CVIU(172), 2018, pp. 67-76.
Elsevier DOI 1812
Visual attention, Image classification, Fully convolutional neural networks BibRef

Palazzo, S.[Simone], Rundo, F.[Francesco], Battiato, S.[Sebastiano], Giordano, D.[Daniela], Spampinato, C.[Concetto],
Visual Saliency Detection guided by Neural Signals,
FG20(525-531)
IEEE DOI 2102
Visualization, Saliency detection, Electroencephalography, Task analysis, Training, Brain modeling, Computational modeling BibRef

Zhu, D.D.[Dan-Dan], Luo, Y.[Ye], Dai, L.[Lei], Shao, X.[Xuan], Zhou, Q.Q.[Qiang-Qiang], Itti, L.[Laurent], Lu, J.W.[Jian-Wei],
Salient object detection via a local and global method based on deep residual network,
JVCIR(54), 2018, pp. 1-9.
Elsevier DOI 1806
BibRef
Earlier: A1, A2, A4, A6, A7, Only:
Saliency prediction based on new deep multi-layer convolution neural network,
ICIP17(2711-2715)
IEEE DOI 1803
Salient object detection, Deep residual network, Local and global features. Computational modeling, Convolution, Feature extraction, Saliency detection, Support vector machines, Training. BibRef

Bak, C., Kocak, A.[Aysun], Erdem, E.[Erkut], Erdem, A.[Aykut],
Spatio-Temporal Saliency Networks for Dynamic Saliency Prediction,
MultMed(20), No. 7, July 2018, pp. 1688-1698.
IEEE DOI 1806
Computational modeling, Dynamics, Feature extraction, Predictive models, Videos, Visualization, Dynamic saliency, deep learning BibRef

Kocak, A.[Aysun], Cizmeciler, K.[Kemal], Erdem, A.[Aykut], Erdem, E.[Erkut],
Top down saliency estimation via superpixel-based discriminative dictionaries,
BMVC14(xx-yy).
HTML Version. 1410
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Yuan, Y.C.[Yu-Chen], Li, C.Y.[Chang-Yang], Kim, J., Cai, W.D.[Wei-Dong], Feng, D.D.[David Dagan],
Dense and Sparse Labeling With Multidimensional Features for Saliency Detection,
CirSysVideo(28), No. 5, May 2018, pp. 1130-1143.
IEEE DOI 1805
Channel estimation, DSL, Estimation, Feature extraction, Image color analysis, Labeling, Neural networks, sparse labeling (SL) BibRef

Yuan, Y.C.[Yu-Chen], Li, C.Y.[Chang-Yang], Kim, J., Cai, W.D.[Wei-Dong], Feng, D.D.[David Dagan],
Reversion Correction and Regularized Random Walk Ranking for Saliency Detection,
IP(27), No. 3, March 2018, pp. 1311-1322.
IEEE DOI 1801
Estimation, Feature extraction, Image color analysis, Image segmentation, Manifolds, Robustness, Visualization, saliency optimization BibRef

Li, C.Y.[Chang-Yang], Yuan, Y.C.[Yu-Chen], Cai, W.D.[Wei-Dong], Xia, Y.[Yong], Feng, D.D.[David Dagan],
Robust saliency detection via regularized random walks ranking,
CVPR15(2710-2717)
IEEE DOI 1510
BibRef

Monroy, R.[Rafael], Lutz, S.[Sebastian], Chalasani, T.[Tejo], Smolic, A.[Aljosa],
SalNet360: Saliency maps for omni-directional images with CNN,
SP:IC(69), 2018, pp. 26-34.
Elsevier DOI 1811
Saliency, Omnidirectional Image (ODI), Convolutional Neural Network (CNN), Virtual Reality (VR) BibRef

Nguyen, A., Kim, J., Oh, H., Kim, H., Lin, W., Lee, S.,
Deep Visual Saliency on Stereoscopic Images,
IP(28), No. 4, April 2019, pp. 1939-1953.
IEEE DOI 1901
convolution, feature extraction, feedforward neural nets, image restoration, learning (artificial intelligence), deep learning BibRef

Chen, J.Z.[Jia-Zhong], Li, Q.Q.[Qing-Qing], Li, P.[Ping], Han, Y.[Yu], Wu, L.[Lei], Ling, H.F.[He-Fei], Wu, W.M.[Wei-Min],
Saliency prediction by Mahalanobis distance of topological feature on deep color components,
JVCIR(60), 2019, pp. 149-157.
Elsevier DOI 1903
Saliency, Deep color components, Topological feature, Covariance matrix, Mahalanobis distance BibRef

Wang, X.[Xiao], Sun, T.[Tao], Yang, R.[Rui], Li, C.L.[Cheng-Long], Luo, B.[Bin], Tang, J.[Jin],
Quality-aware dual-modal saliency detection via deep reinforcement learning,
SP:IC(75), 2019, pp. 158-167.
Elsevier DOI 1906
Dual-modal saliency detection, Deep reinforcement learning, Quality-aware fusion, Generative adversarial networks BibRef

Wang, X.F.[Xiao-Fan], Li, S.J.[Sheng-Jie],
Image saliency prediction by learning deep probability model,
SP:IC(78), 2019, pp. 471-476.
Elsevier DOI 1909
Image saliency prediction, Probability model, Deeply-learned feature BibRef

Fan, Y.J.[Ya Ju],
Autoencoder node saliency: Selecting relevant latent representations,
PR(88), 2019, pp. 643-653.
Elsevier DOI 1901
Autoencoder, Latent representations, Unsupervised learning, Neural networks, Node selection, Model interpretation BibRef

Xu, Y.Y.[Yan-Yu], Gao, S.H.[Sheng-Hua], Wu, J.R.[Jun-Ru], Li, N.Y.[Nian-Yi], Yu, J.Y.[Jing-Yi],
Personalized Saliency and Its Prediction,
PAMI(41), No. 12, December 2019, pp. 2975-2989.
IEEE DOI 1911
Observers, Saliency detection, Feature extraction, Visualization, Semantics, Predictive models, Image color analysis, convolutional neural network BibRef

Zhang, Y., Zhang, S., Zhang, P., Song, H., Zhang, X.,
Local Regression Ranking for Saliency Detection,
IP(29), No. , 2020, pp. 1536-1547.
IEEE DOI 1911
Feature extraction, Saliency detection, Laplace equations, Extraterrestrial measurements, Neural networks, Kernel, saliency propagation BibRef

Li, H.[Hao], Qi, F.[Fei], Shi, G.M.[Guang-Ming], Lin, C.H.[Chun-Huan],
A multiscale dilated dense convolutional network for saliency prediction with instance-level attention competition,
JVCIR(64), 2019, pp. 102611.
Elsevier DOI 1911
Saliency, Attention competition, Convolutional neural networks, Dense connections, Dilated convolution, Multiscale features BibRef

Ji, C.[Chao], Huang, X.B.[Xin-Bo], Cao, W.[Wen], Zhu, Y.C.[Yong-Can], Zhang, Y.[Ye],
Saliency detection using Multi-layer graph ranking and combined neural networks,
JVCIR(65), 2019, pp. 102673.
Elsevier DOI 1912
Machine vision, Saliency detection, Fast R-CNN, Region Net, Local-Global Net BibRef

Cai, Y., Dai, L., Wang, H., Chen, L., Li, Y.,
A Novel Saliency Detection Algorithm Based on Adversarial Learning Model,
IP(29), 2020, pp. 4489-4504.
IEEE DOI 2003
Adversarial learning model, discriminative models generative model, saliency detection BibRef

Jia, S.[Sen], Bruce, N.D.B.[Neil D.B.],
EML-NET: An Expandable Multi-Layer NETwork for saliency prediction,
IVC(95), 2020, pp. 103887.
Elsevier DOI 2004
Saliency detection, Scalability, Loss function BibRef

Wu, Y.[Yong], Liu, Z.[Zhi], Zhou, X.F.[Xiao-Fei],
Saliency detection using adversarial learning networks,
JVCIR(67), 2020, pp. 102761.
Elsevier DOI 2004
Adversarial learning, Generator, discriminator, Saliency detection, Feedback information BibRef

Zolna, K.[Konrad], Geras, K.J.[Krzysztof J.], Cho, K.[Kyunghyun],
Classifier-agnostic saliency map extraction,
CVIU(196), 2020, pp. 102969.
Elsevier DOI 2006
Saliency map, Convolutional neural networks, Image classification, Weakly supervised localization BibRef

Radvanyi, M.[Mihaly], Karacs, K.[Kristof],
Peeling off image layers on topographic architectures,
PRL(135), 2020, pp. 50-56.
Elsevier DOI 2006
Cellular neural network, Computer vision, Image hierarchy, Saliency, Topographic processor BibRef

Yang, S., Lin, G., Jiang, Q., Lin, W.,
A Dilated Inception Network for Visual Saliency Prediction,
MultMed(22), No. 8, August 2020, pp. 2163-2176.
IEEE DOI 2007
Visualization, Computational modeling, Predictive models, Feature extraction, Spatial resolution, Computer architecture, inception module BibRef

Huang, L., Song, K., Gong, A., Liu, C., Yan, Y.,
RGB-T Saliency Detection via Low-Rank Tensor Learning and Unified Collaborative Ranking,
SPLetters(27), 2020, pp. 1585-1589.
IEEE DOI 2009
Saliency detection, Tensile stress, Feature extraction, Signal processing algorithms, Collaboration, collaborative ranking BibRef

Qiu, W.L.[Wen-Liang], Gao, X.[Xinbo], Han, B.[Bing],
Saliency detection using a deep conditional random field network,
PR(103), 2020, pp. 107266.
Elsevier DOI 2005
Saliency detection, Conditional random field, Convolutional neural network BibRef

Liang, Y.[Ye], Liu, H.Z.[Hong-Zhe], Ma, N.[Nan],
A novel deep network and aggregation model for saliency detection,
VC(36), No. 9, September 2020, pp. 1883-1895.
WWW Link. 2008
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Zhou, F., Yao, R., Liao, G., Liu, B., Qiu, G.,
Visual Saliency via Embedding Hierarchical Knowledge in a Deep Neural Network,
IP(29), 2020, pp. 8490-8505.
IEEE DOI 2008
Feature fusion, hierarchical knowledge embedding, saliency map, task-independent saliency BibRef

Liu, N.[Nian], Han, J.W.[Jun-Wei], Yang, M.H.[Ming-Hsuan],
PiCANet: Pixel-Wise Contextual Attention Learning for Accurate Saliency Detection,
IP(29), 2020, pp. 6438-6451.
IEEE DOI 2007
BibRef
Earlier:
PiCANet: Learning Pixel-Wise Contextual Attention for Saliency Detection,
CVPR18(3089-3098)
IEEE DOI 1812
Feature extraction, Saliency detection, Object detection, Context modeling, Convolution, Semantics, Computational modeling, object detection. Feature extraction, Task analysis, Visualization, Dogs BibRef

Pang, Y.[Yu], Yu, X.S.[Xiao-Sheng], Wu, Y.H.[Yun-He], Wu, C.D.[Cheng-Dong], Jiang, Y.[Yang],
Bagging-based saliency distribution learning for visual saliency detection,
SP:IC(87), 2020, pp. 115928.
Elsevier DOI 2007
Saliency detection, Prior knowledge, Bagging method, Saliency distribution learning, Saliency optimization, Prejudgment mechanism BibRef

Borji, A.[Ali],
Saliency Prediction in the Deep Learning Era: Successes and Limitations,
PAMI(43), No. 2, February 2021, pp. 679-700.
IEEE DOI 2101
Predictive models, Computational modeling, Benchmark testing, Data models, Visualization, Deep learning, Task analysis, deep learning BibRef


Ye, P., Wang, Y., Xia, Y.,
Enhanced Saliency Prediction via Orientation Selectivity,
VCIP20(91-95)
IEEE DOI 2102
Visualization, Feature extraction, Sensitivity, Predictive models, Image edge detection, Computational modeling, Visual systems, Visual Error Sensitivity BibRef

Wang, L., Shen, L.,
A ConvLSTM-Combined Hierarchical Attention Network For Saliency Detection,
ICIP20(1996-2000)
IEEE DOI 2011
Feature extraction, Saliency detection, Neural networks, Object detection, Image edge detection, Convolution, Visualization, Saliency Detection BibRef

Luo, Y.[Yan], Wong, Y.K.[Yong-Kang], Kankanhalli, M.S.[Mohan S.], Zhao, Q.[Qi],
n-reference Transfer Learning for Saliency Prediction,
ECCV20(VIII:502-519).
Springer DOI 2011
BibRef

Ramanathan, V., Dwivedi, P., Katabathuni, B., Chakraborty, A., Thakur, C.S.,
QUICKSAL: A small and sparse visual saliency model for efficient inference in resource constrained hardware,
WACV20(1667-1677)
IEEE DOI 2006
Visualization, Computational modeling, Feature extraction, Standards, Neural networks, Kernel, Hardware BibRef

Piao, Y.R.[Yong-Ri], Ji, W.[Wei], Li, J.J.[Jing-Jing], Zhang, M.[Miao], Lu, H.C.[Hu-Chuan],
Depth-Induced Multi-Scale Recurrent Attention Network for Saliency Detection,
ICCV19(7253-7262)
IEEE DOI 2004
feature extraction, image colour analysis, image fusion, learning (artificial intelligence), BibRef

Zhao, T.[Ting], Wu, X.Q.[Xiang-Qian],
Pyramid Feature Attention Network for Saliency Detection,
CVPR19(3080-3089).
IEEE DOI 2002
BibRef

Zhang, K.[Kaihua], Li, T.[Tengpeng], Liu, B.[Bo], Liu, Q.S.[Qing-Shan],
Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing,
CVPR19(3090-3099).
IEEE DOI 2002
BibRef

He, S.[Sen], Tavakoli, H.R.[Hamed R.], Borji, A.[Ali], Mi, Y.[Yang], Pugeault, N.[Nicolas],
Understanding and Visualizing Deep Visual Saliency Models,
CVPR19(10198-10207).
IEEE DOI 2002
BibRef

Zhu, C., Yan, W., Liu, S., Li, T., Li, G.,
Salient Contour-Aware Based Twice Learning Strategy for Saliency Detection,
ACVR19(2541-2548)
IEEE DOI 2004
computer vision, convolutional neural nets, learning (artificial intelligence), object detection, twice learning strategy BibRef

Zhang, J., Zhang, T., Daf, Y., Harandi, M., Hartley, R.,
Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective,
CVPR18(9029-9038)
IEEE DOI 1812
Noise measurement, Saliency detection, Predictive models, Training, Neural networks, Optimization, Labeling BibRef

Kong, P.[Phutphalla], Mancas, M.[Matei], Thuon, N.[Nimol], Kheang, S.[Seng], Gosselin, B.[Bernard],
Do Deep-Learning Saliency Models Really Model Saliency?,
ICIP18(2331-2335)
IEEE DOI 1809
Detectors, Face, Measurement, Visualization, Animals, Transportation, Computational modeling, attention, saliency, DNN, bottom-up, top-down, text detection BibRef

Wang, T., Zhang, L., Wang, S., Lu, H., Yang, G., Ruan, X., Borji, A.,
Detect Globally, Refine Locally: A Novel Approach to Saliency Detection,
CVPR18(3127-3135)
IEEE DOI 1812
Convolution, Saliency detection, Visualization, Feature extraction, Neural networks, Object detection, Semantics BibRef

Zeng, Y., Lu, H., Zhang, L., Feng, M., Borji, A.,
Learning to Promote Saliency Detectors,
CVPR18(1644-1653)
IEEE DOI 1812
Training, Saliency detection, Feature extraction, Artificial neural networks, Testing, Task analysis, Measurement BibRef

Yan, B.[Bing], Wang, H.Q.[Hao-Qian], Wang, X.Z.[Xing-Zheng], Zhang, Y.B.[Yong-Bing],
An accurate saliency prediction method based on generative adversarial networks,
ICIP17(2339-2343)
IEEE DOI 1803
Cost function, Feature extraction, Games, Task analysis, Training, Visualization, GAN, Saliency prediction, saliency network BibRef

Imamoglu, N., Zhang, C., Shmoda, W., Fang, Y., Shi, B.,
Saliency detection by forward and backward cues in deep-CNN,
ICIP17(430-434)
IEEE DOI 1803
convolutional neural networks, partially-guided back-propagation, saliency detection BibRef

Jin, G., Shen, S., Zhang, D., Duan, W., Zhang, Y.,
Deep saliency map estimation of hand-crafted features,
ICIP17(4262-4266)
IEEE DOI 1803
Benchmark testing, Estimation, Feature extraction, Machine learning, Pipelines, Saliency detection, Visualization, Saliency detection BibRef

Zhu, L., Ling, H., Wu, J., Deng, H., Liu, J.,
Saliency Pattern Detection by Ranking Structured Trees,
ICCV17(5468-5477)
IEEE DOI 1802
feature extraction, image representation, learning (artificial intelligence), object detection, Proposals BibRef

Cornia, M., Baraldi, L., Serra, G., Cucchiara, R.,
A deep multi-level network for saliency prediction,
ICPR16(3488-3493)
IEEE DOI 1705
Benchmark testing, Computer architecture, Convolutional codes, Encoding, Feature extraction, Measurement, Observers BibRef

Pan, J., Sayrol, E., Giró-i-Nieto, X.[Xavier], McGuinness, K.[Kevin], O'Connor, N.E.,
Shallow and Deep Convolutional Networks for Saliency Prediction,
CVPR16(598-606)
IEEE DOI 1612
BibRef

Salvador, A., Giró-i-Nieto, X.[Xavier], Marqués, F.[Ferran],
Faster R-CNN Features for Instance Search,
DeepLearn-C16(394-401)
IEEE DOI 1612
BibRef

Cornia, M.[Marcella], Baraldi, L.[Lorenzo], Serra, G.[Giuseppe], Cucchiara, R.[Rita],
Multi-level Net: A Visual Saliency Prediction Model,
ACVR16(II: 302-315).
Springer DOI 1611
BibRef

Marighetto, P., Abdelkader, I.H., Duzelier, S., Décombas, M., Riche, N., Jakubowicz, J., Mancas, M., Gosselin, B., Laganière, R.,
FUNNRAR: Hybrid rarity/learning visual saliency,
ICIP16(2782-2786)
IEEE DOI 1610
Artificial neural networks BibRef

Huang, X., Shen, C., Boix, X., Zhao, Q.,
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks,
ICCV15(262-270)
IEEE DOI 1602
Computational modeling BibRef

Zhang, D.W.[Ding-Wen], Han, J.W.[Jun-Wei], Li, C.[Chao], Wang, J.D.[Jing-Dong],
Co-saliency detection via looking deep and wide,
CVPR15(2994-3002)
IEEE DOI 1510
BibRef

Wang, L.J.[Li-Jun], Lu, H.C.[Hu-Chuan], Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Deep networks for saliency detection via local estimation and global search,
CVPR15(3183-3192)
IEEE DOI 1510
BibRef

Vig, E.[Eleonora], Dorr, M.[Michael], Cox, D.[David],
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images,
CVPR14(2798-2805)
IEEE DOI 1409
deep learning; hyperparameter optimization; saliency BibRef

Li, G.B.[Guan-Bin], Yu, Y.Z.[Yi-Zhou],
Visual Saliency Detection Based on Multiscale Deep CNN Features,
IP(25), No. 11, November 2016, pp. 5012-5024.
IEEE DOI 1610
BibRef
And:
Deep Contrast Learning for Salient Object Detection,
CVPR16(478-487)
IEEE DOI 1612
BibRef
Earlier:
Visual saliency based on multiscale deep features,
CVPR15(5455-5463)
IEEE DOI 1510
feature extraction BibRef

Liu, R.S.[Ri-Sheng], Cao, J.J.[Jun-Jie], Lin, Z.C.[Zhou-Chen], Shan, S.G.[Shi-Guang],
Adaptive Partial Differential Equation Learning for Visual Saliency Detection,
CVPR14(3866-3873)
IEEE DOI 1409
Learning-Based PDEs; Saliency Detection; Submodular Optimization BibRef

Hong, Y.[Yi], Jiang, J.Y.[Jia-Yan], Tu, Z.W.[Zhuo-Wen],
Sparse semi-supervised learning for perceptual grouping,
POCV10(1-8).
IEEE DOI 1006
BibRef

Orabona, F.[Francesco], Metta, G.[Giorgio], Sandini, G.[Giulio],
Learning Association Fields from Natural Images,
PercOrg06(174).
IEEE DOI 0609
BibRef

Driancourt, R.[Remi],
Learning Perceptual Organization with a Developmental Robot,
PercOrg04(60).
IEEE DOI 0502
BibRef

Derou, D.[Dominique], Herault, L.[Laurent],
Pulsed neural networks and perceptive grouping,
ECCV94(A:521-526).
Springer DOI 9405
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Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Grouping, Lines and Curves .


Last update:Sep 19, 2021 at 21:11:01