14.5.7.3.1 Recurrent Neural Networks for Shapes and Complex Features

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Feature Description. Recurrent Neural Networks. A subset.

Varoglu, E., Hacioglu, K.,
Recurrent neural network speech predictor based on dynamical systems approach,
VISP(147), No. 2, April 2000, pp. 149. 0005
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Gupta, L.[Lalit], McAvoy, M.[Mark],
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Gupta, L.[Lalit], McAvoy, M.[Mark], Phegley, J.[James],
Classification of temporal sequences via prediction using the simple recurrent neural network,
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Elsevier DOI 0006
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Minimal model dimension/order determination algorithms for recurrent neural networks,
PRL(30), No. 9, 1 July 2009, pp. 812-819.
Elsevier DOI 0905
Model dimension/order determination; Nonlinear system identification; Recurrent neural networks; Minimal realization BibRef

Chatzis, S.P.[Sotirios P.], Demiris, Y.[Yiannis],
The copula echo state network,
PR(45), No. 1, 2012, pp. 570-577.
Elsevier DOI 1410
Copula Echo state networks for Recurrent NN training. BibRef

Shuai, B.[Bing], Zuo, Z.[Zhen], Wang, G.[Gang],
Quaddirectional 2D-Recurrent Neural Networks For Image Labeling,
SPLetters(22), No. 11, November 2015, pp. 1990-1994.
IEEE DOI 1509
feature extraction See also Exemplar based Deep Discriminative and Shareable Feature Learning for scene image classification. BibRef

Zuo, Z.[Zhen], Shuai, B.[Bing], Wang, G.[Gang], Liu, X.[Xiao], Wang, X.X.[Xing-Xing], Wang, B.[Bing], Chen, Y.S.[Yu-Shi],
Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks,
IP(25), No. 7, July 2016, pp. 2983-2996.
IEEE DOI 1606
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Earlier:
Convolutional recurrent neural networks: Learning spatial dependencies for image representation,
DeepLearn15(18-26)
IEEE DOI 1510
computational complexity. Computational modeling BibRef

Shuai, B.[Bing], Zuo, Z.[Zhen], Wang, B.[Bing], Wang, G.[Gang],
Scene Segmentation with DAG-Recurrent Neural Networks,
PAMI(40), No. 6, June 2018, pp. 1480-1493.
IEEE DOI 1805
BibRef
Earlier:
DAG-Recurrent Neural Networks for Scene Labeling,
CVPR16(3620-3629)
IEEE DOI 1612
Context, Context modeling, Image segmentation, Neural networks, Object segmentation, Semantics, Training, CNN, COCO stuff, DAG-RNN, sift flow BibRef

Mou, L., Ghamisi, P., Zhu, X.X.,
Unsupervised Spectral -Spatial Feature Learning via Deep Residual Conv -Deconv Network for Hyperspectral Image Classification,
GeoRS(56), No. 1, January 2018, pp. 391-406.
IEEE DOI 1801
Feature extraction, Hyperspectral imaging, Network architecture, Support vector machines, Training, Convolutional network, unsupervised spectral-spatial feature learning BibRef

Gao, Q.S.[Qi-Shuo], Lim, S.[Samsung], Jia, X.P.[Xiu-Ping],
Hyperspectral Image Classification Using Convolutional Neural Networks and Multiple Feature Learning,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Chen, Y.S.[Yu-Shi], Jiang, H.L.[Han-Lu], Li, C.Y.[Chun-Yang], Jia, X.P.[Xiu-Ping], Ghamisi, P.[Pedram],
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks,
GeoRS(54), No. 10, October 2016, pp. 6232-6251.
IEEE DOI 1610
feature extraction BibRef

Mou, L., Ghamisi, P.[Pedram], Zhu, X.X.,
Deep Recurrent Neural Networks for Hyperspectral Image Classification,
GeoRS(55), No. 7, July 2017, pp. 3639-3655.
IEEE DOI 1706
BibRef
And: Corrections: GeoRS(56), No. 2, February 2018, pp. 1214-1215.
IEEE DOI 1802
Data models, Hyperspectral imaging, Logic gates, Recurrent neural networks, Support vector machines, Convolutional neural network (CNN), deep learning, gated recurrent unit (GRU), hyperspectral image classification, long short-term memory (LSTM), recurrent neural network (RNN) BibRef

Abdulnabi, A.H., Shuai, B., Zuo, Z., Chau, L.P., Wang, G.,
Multimodal Recurrent Neural Networks With Information Transfer Layers for Indoor Scene Labeling,
MultMed(20), No. 7, July 2018, pp. 1656-1671.
IEEE DOI 1806
Adaptation models, Context modeling, Feature extraction, Kernel, Labeling, Recurrent neural networks, CNNs, Multimodal learning, RNNs BibRef

Lakhal, M.I.[Mohamed Ilyes], «evikalp, H.[Hakan], Escalera, S.[Sergio], Ofli, F.[Ferda],
Recurrent neural networks for remote sensing image classification,
IET-CV(12), No. 7, October 2018, pp. 1040-1045.
DOI Link 1809
BibRef


Ye, X.Q.[Xiao-Qing], Li, J.[Jiamao], Huang, H.[Hexiao], Du, L.[Liang], Zhang, X.L.[Xiao-Lin],
3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation,
ECCV18(VII: 415-430).
Springer DOI 1810
BibRef

Sadeghian, A.[Amir], Legros, F.[Ferdinand], Voisin, M.[Maxime], Vesel, R.[Ricky], Alahi, A.[Alexandre], Savarese, S.[Silvio],
CAR-Net: Clairvoyant Attentive Recurrent Network,
ECCV18(XI: 162-180).
Springer DOI 1810
BibRef

Raue, F.[Federico], Byeon, W.[Wonmin], Breuel, T.M.[Thomas M.], Liwicki, M.[Marcus],
Parallel sequence classification using recurrent neural networks and alignment,
ICDAR15(581-585)
IEEE DOI 1511
BibRef

Tavakoli, H.R.[Hamed R.], Borji, A.[Ali], Anwer, R.M.[Rao Muhammad], Rahtu, E.[Esa], Kannala, J.[Juho],
Bottom-Up Attention Guidance for Recurrent Image Recognition,
ICIP18(3004-3008)
IEEE DOI 1809
Computational modeling, Task analysis, Computer architecture, Image recognition, Predictive models, Pipelines, deep neural networks BibRef

Zhao, Z., Wu, X., Chen, P.C.Y., Chen, W.,
General Recurrent Attention Model for Jointly Multiple Object Recognition and Weakly Supervised Localization,
ICIP18(341-345)
IEEE DOI 1809
Erbium, Indexes, Attention, Recognition, Localization, Reinforcement Learning BibRef

Wang, Q., Li, P.,
D-LSM: Deep Liquid State Machine with unsupervised recurrent reservoir tuning,
ICPR16(2652-2657)
IEEE DOI 1705
Biological neural networks, Convolution, Feature extraction, Kernel, Liquids, Neurons, Reservoirs BibRef

Gandhi, A.[Ankit], Sharma, A.[Arjun], Biswas, A.[Arijit], Deshmukh, O.[Om],
GeThR-Net: A Generalized Temporally Hybrid Recurrent Neural Network for Multimodal Information Fusion,
CVAVM16(II: 883-899).
Springer DOI 1611
BibRef

Luo, W.X.[Wei-Xin], Liu, W.[Wen], Gao, S.H.[Sheng-Hua],
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework,
ICCV17(341-349)
IEEE DOI 1802
compressed sensing, feature extraction, image reconstruction, learning (artificial intelligence), recurrent neural nets, Training BibRef

Aviles, A.I., Marban, A., Sobrevilla, P., Fernandez, J., Casals, A.,
A recurrent neural network approach for 3D vision-based force estimation,
IPTA14(1-6)
IEEE DOI 1503
dexterous manipulators BibRef

Hillar, C.[Christopher], Mehta, R.[Ram], Koepsell, K.[Kilian],
A hopfield recurrent neural network trained on natural images performs state-of-the-art image compression,
ICIP14(4092-4096)
IEEE DOI 1502
Image coding BibRef

Prokhorov, D.V.[Danil V.],
Object recognition in 3D lidar data with recurrent neural network,
OTCBVS09(9-15).
IEEE DOI 0906
BibRef

Chen, J.M.[Jin-Miao], Chaudhari, N.S.,
Improvement of bidirectional recurrent neural network for learning long-term dependencies,
ICPR04(IV: 593-596).
IEEE DOI 0409
BibRef

Morita, S.[Satoru],
Learning Behavior Using Multiresolution Recurrent Neural Network,
CAIP99(157-166).
Springer DOI 9909
BibRef

Song, H.H., Lee, S.W.,
A New Recurrent Neural Network Architecture for Pattern Recognition,
ICPR96(IV: 718-722).
IEEE DOI 9608
(Korea Univ., KOR) BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks for Shapes and Complex Features .


Last update:Oct 15, 2018 at 09:19:25