10.1.10.3 Matching for Stereo, Neural Network Applications, CNN

Chapter Contents (Back)
Stereo, Matching. Neural Networks.

Marshall, J.A., Kalarickal, G.J., Graves, E.B.,
Neural Model of Visual Stereomatching: Slant, Transparency and Clouds,
NetCompNeur(7), No. 4, November 1996, pp. 635-669. 9701
BibRef

Lee, J.J.[Jun Jae], Shim, J.C.[Jae Chang], Ha, Y.H.[Yeong Ho],
Stereo Correspondence Using the Hopfield Neural-Network of a New Energy Function,
PR(27), No. 11, November 1994, pp. 1513-1522.
Elsevier DOI BibRef 9411

de la Cruz, J.M., Pajares, G., Aranda, J.,
A Neural-Network Model in Stereovision Matching,
NeurNet(8), No. 5, 1995, pp. 805-813. BibRef 9500

Pajares, G., de la Cruz, J.M., Aranda, J.,
Stereo Matching Based on the Self-Organizing Feature-Mapping Algorithm,
PRL(19), No. 3-4, March 1998, pp. 319-330. 9807
BibRef

Pajares, G.[Gonzalo], de la Cruz, J.M.[Jesús M.], Aranda, J.[Joaquin],
Relaxation by Hopfield Network in Stereo Image Matching,
PR(31), No. 5, May 1998, pp. 561-574.
Elsevier DOI 9805
BibRef

Pajares, G.[Gonzalo], de la Cruz, J.M.[Jesús Manuel], López-Orozco, J.A.[José Antonio],
Relaxation labeling in stereo image matching,
PR(33), No. 1, January 2000, pp. 53-68.
Elsevier DOI 0005

See also Fuzzy Cognitive Maps for stereovision matching. BibRef

Pajares, G.[Gonzalo], López-Orozco, J.A.[José Antonio], de la Cruz, J.M.[Jesús Manuel],
Improving Stereovision Matching through Supervised Learning,
PAA(1), No. 2, 1998, pp. 105-120. BibRef 9800

Pajares, G.[Gonzalo], de la Cruz, J.M.[Jesús Manuel],
Stereovision matching through support vector machines,
PRL(24), No. 15, November 2003, pp. 2575-2583.
Elsevier DOI 0308
Local matching. BibRef

Pajares, G.[Gonzalo], de la Cruz, J.M.[Jesús Manuel],
On Combining Support Vector Machines and Simulated Annealing in Stereovision Matching,
SMC-B(34), No. 4, August 2004, pp. 1646-1657.
IEEE Abstract. 0409
BibRef

Pajares, G.[Gonzalo], de la Cruz, J.M.[Jesus M.],
Pattern Recognition Learning Applied to Stereovision Matching,
ICPR98(PRP1). 9808
Not online. BibRef

Pajares, G.[Gonzalo], de la Cruz, J.M.[Jesús M.],
Fuzzy Cognitive Maps for stereovision matching,
PR(39), No. 11, November 2006, pp. 2101-2114.
Elsevier DOI 0608
Fuzzy Cognitive Maps; Fuzzy Clustering; Relaxation; Fuzzy; Stereovision; Matching; Similarity; Smoothness; Ordering; Epipolar; Uniqueness
See also Relaxation labeling in stereo image matching. BibRef

Herrera, P.J.[P. Javier], Pajares, G.[Gonzalo], Guijarro, M.[María], Ruz, J.J.[J. Jaime], de la Cruz, J.M.[Jesús M.],
Combination of Attributes in Stereovision Matching for Fish-Eye Lenses in Forest Analysis,
ACIVS09(277-287).
Springer DOI 0909
BibRef

Pajares, G.[Gonzalo], de la Cruz, J.M.[Jesús M.],
Local stereovision matching through the ADALINE neural network,
PRL(22), No. 14, December 2001, pp. 1457-1473.
Elsevier DOI 0110
BibRef

Wang, Z.[Zhi], Zhu, S.Q.[Shi-Qiang], Li, Y.H.[Yue-Hua], Cui, Z.Z.[Zheng-Zhe],
Convolutional neural network based deep conditional random fields for stereo matching,
JVCIR(40, Part B), No. 1, 2016, pp. 739-750.
Elsevier DOI 1610
Stereo matching BibRef

Brandao, P.[Patrick], Mazomenos, E.[Evangelos], Stoyanov, D.[Danail],
Widening siamese architectures for stereo matching,
PRL(120), 2019, pp. 75-81.
Elsevier DOI 1904
Stereo matching, Convolutional neural network, Disparity, Computer vision BibRef

Nguyen, T.P.[Tien Phuoc], Jeon, J.W.[Jae Wook],
Wide context learning network for stereo matching,
SP:IC(78), 2019, pp. 263-273.
Elsevier DOI 1909
Stereo matching, 3D reconstruction, Matching cost, Cost aggregation, Convolutional neural network BibRef

Lei, J.F.[Jun-Feng], Dong, Y.X.[Yu-Xuan], Zhao, T.[Tao], xiao, J.S.[Jin-Sheng], Chen, Y.H.[Yun-Hua], Li, B.J.[Bi-Jun],
Novel shrinking residual convolutional neural network for efficient accurate stereo matching,
JVCIR(72), 2020, pp. 102872.
Elsevier DOI 2010
Stereo matching, Matching cost, Residual convolutional neural network BibRef

Liang, Z.F.[Zheng-Fa], Guo, Y.L.[Yu-Lan], Feng, Y.L.[Yi-Liu], Chen, W.[Wei], Qiao, L.B.[Lin-Bo], Zhou, L.[Li], Zhang, J.F.[Jian-Feng], Liu, H.Z.[Heng-Zhu],
Stereo Matching Using Multi-Level Cost Volume and Multi-Scale Feature Constancy,
PAMI(43), No. 1, January 2021, pp. 300-315.
IEEE DOI 2012
Estimation, Feature extraction, Image reconstruction, Convolution, Task analysis, Convolutional neural networks, Training, feature constancy BibRef

Zhang, Y.[Yaru], Li, Y.[Yaqian], Wu, C.[Chao], Liu, B.[Bin],
Attention-guided aggregation stereo matching network,
IVC(106), 2021, pp. 104088.
Elsevier DOI 2102
Convolutional neural network, Stereo matching, Attention mechanism, Guided cost volume BibRef

Sang, H.W.[Hai-Wei], Yang, Z.L.[Zu-Liu], Yang, X.W.[Xiao-Wei], Zhao, Y.[Yong],
MPA-Net: multi-path attention stereo matching network,
IET-IPR(14), No. 17, 24 December 2020, pp. 4554-4562.
DOI Link 2104
BibRef

Shao, X.T.[Xiao-Tao], Zhang, W.[Wen], Guo, M.K.[Ming-Kun], Guo, S.Q.[Si-Qi], Qian, M.Y.[Man-Yi],
CSNet: Cascade stereo matching network using multi-information cost volume,
IET-ITS(15), No. 5, 2021, pp. 635-645.
DOI Link 2106
BibRef


Hou, Y.X.[Yu-Xin], Janjua, M.K.[Muhammad Kamran], Kannala, J.H.[Ju-Ho], Solin, A.[Arno],
Movement-induced Priors for Deep Stereo,
ICPR21(3628-3635)
IEEE DOI 2105
Training, Micromechanical devices, Handheld computers, Estimation, Gaussian processes, Sensors BibRef

Hirner, D.[Dominik], Fraundorfer, F.[Friedrich],
FC-DCNN: A densely connected neural network for stereo estimation,
ICPR21(2482-2489)
IEEE DOI 2105
Matched filters, Image segmentation, Filtering, Neural networks, Estimation, Benchmark testing BibRef

Poggi, M.[Matteo], Tosi, F.[Fabio], Aleotti, F.[Filippo], Mattoccia, S.[Stefano],
Leveraging a weakly adversarial paradigm for joint learning of disparity and confidence estimation,
ICPR21(270-277)
IEEE DOI 2105
Training, Wide area networks, Uncertainty, Estimation, Deep architecture, Training data, Pattern recognition BibRef

Zhang, J.Y.[Jing-Yang], Yao, Y.[Yao], Luo, Z.[Zixin], Li, S.[Shiwei], Shen, T.[Tianwei], Fang, T.[Tian], Quan, L.[Long],
Learning Stereo Matchability in Disparity Regression Networks,
ICPR21(1611-1618)
IEEE DOI 2105
Training, Refining, Benchmark testing, Attenuation, Entropy, Pattern recognition BibRef

Beaupre, D.A.[David-Alexandre], Bilodeau, G.A.[Guillaume-Alexandre],
Domain Siamese CNNs for Sparse Multispectral Disparity Estimation,
ICPR21(3667-3674)
IEEE DOI 2105
Visualization, Correlation, Merging, Estimation, Feature extraction, Robustness, Surface texture BibRef

Xing, J.B.[Jia-Bin], Qi, Z.[Zhi], Dong, J.Y.[Ji-Ying], Cai, J.X.[Jia-Xuan], Liu, H.[Hao],
Mabnet: A Lightweight Stereo Network Based on Multibranch Adjustable Bottleneck Module,
ECCV20(XXVIII:340-356).
Springer DOI 2011
BibRef

Zhang, F.H.[Fei-Hu], Qi, X.J.[Xiao-Juan], Yang, R.G.[Rui-Gang], Prisacariu, V.[Victor], Wah, B.[Benjamin], Torr, P.H.S.[Philip H.S.],
Domain-invariant Stereo Matching Networks,
ECCV20(II:420-439).
Springer DOI 2011
BibRef

Chen, Y.[Yang], Lu, Z.Q.[Zong-Qing], Zhang, X.C.[Xue-Chen], Chen, L.[Lei], Liao, Q.M.[Qing-Min],
Noise-Sampling Cross Entropy Loss: Improving Disparity Regression Via Cost Volume Aware Regularizer,
ICIP20(2780-2784)
IEEE DOI 2011
Entropy, Estimation, Laplace equations, Neural networks, Machine learning, Coherence, Training, Stereo matching, cost volume, noise-sampling BibRef

Liu, R., Yang, C., Sun, W., Wang, X., Li, H.,
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching,
CVPR20(12754-12763)
IEEE DOI 2008
Training, Optimization, Estimation, Neural networks, Correlation, Pattern matching, Distortion BibRef

Xu, H., Zhang, J.,
AANet: Adaptive Aggregation Network for Efficient Stereo Matching,
CVPR20(1956-1965)
IEEE DOI 2008
Feature extraction, Adaptive systems, Correlation, Neural networks BibRef

Renteria-Vidales, O.I., Cuevas-Tello, J.C., Reyes-Figueroa, A., Rivera, M.,
Modulenet: A Convolutional Neural Network for Stereo Vision,
MCPR20(219-228).
Springer DOI 2007
BibRef

Zhang, F.H.[Fei-Hu], Prisacariu, V.[Victor], Yang, R.G.[Rui-Gang], Torr, P.H.S.[Philip H.S.],
GA-Net: Guided Aggregation Net for End-To-End Stereo Matching,
CVPR19(185-194).
IEEE DOI 2002
BibRef

Wu, Z., Wu, X., Zhang, X., Wang, S., Ju, L.,
Semantic Stereo Matching With Pyramid Cost Volumes,
ICCV19(7483-7492)
IEEE DOI 2004
convolutional neural nets, feature extraction, image matching, image segmentation, learning (artificial intelligence), Optical imaging BibRef

Chen, C., Chen, X., Cheng, H.,
On the Over-Smoothing Problem of CNN Based Disparity Estimation,
ICCV19(8996-9004)
IEEE DOI 2004
Code, Stereo.
WWW Link. convolutional neural nets, estimation theory, image segmentation, learning (artificial intelligence), probability, Entropy BibRef

Liang, Z., Feng, Y., Guo, Y., Liu, H., Chen, W., Qiao, L., Zhou, L., Zhang, J.,
Learning for Disparity Estimation Through Feature Constancy,
CVPR18(2811-2820)
IEEE DOI 1812
Estimation, Convolution, Correlation, Feature extraction, Image reconstruction, Computer architecture, Network architecture BibRef

Seabright, M., Streeter, L., Cree, M., Duke, M., Tighe, R.,
Simple Stereo Matching Algorithm for Localising Keypoints in a Restricted Search Space,
IVCNZ18(1-6)
IEEE DOI 1902
Cameras, Geometry, Calibration, Approximation algorithms, Search problems, Yield estimation, convolutional neural networks BibRef

Mao, W., Gong, M.,
Disparity Filtering with 3D Convolutional Neural Networks,
CRV18(246-253)
IEEE DOI 1812
Training, Optimization, Computer architecture, 3D CNNs BibRef

Moskvyak, O.[Olga], Maire, F.[Frederic],
Learning Geometric Equivalence between Patterns Using Embedding Neural Networks,
DICTA17(1-8)
IEEE DOI 1804
Equivalence: 2 dfifferent views of the same object. biology computing, cameras, computer vision, ecology, equivalence classes, geometry, image recognition, Training BibRef

Pang, J., Sun, W., Ren, J.S., Yang, C., Yan, Q.,
Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching,
DeepLearn-G17(878-886)
IEEE DOI 1802
Convolution, Estimation, Optimization, Training BibRef

Zhou, C., Zhang, H., Shen, X., Jia, J.,
Unsupervised Learning of Stereo Matching,
ICCV17(1576-1584)
IEEE DOI 1802
convolution, image matching, iterative methods, neural nets, random processes, stereo image processing, unsupervised learning, Unsupervised learning BibRef

Seki, A.[Akihito], Pollefeys, M.[Marc],
SGM-Nets: Semi-Global Matching with Neural Networks,
CVPR17(6640-6649)
IEEE DOI 1711
BibRef
And:
Patch Based Confidence Prediction for Dense Disparity Map,
BMVC16(xx-yy).
HTML Version. 1805
Semi-Global Matching. Estimation, Image edge detection, Neural networks, Pipelines, Standards, Testing BibRef

Yuan, W., Fan, Z., Yuan, X., Gong, J., Shibasaki, R.,
Unsupervised Multi-constraint Deep Neural Network for Dense Image Matching,
ISPRS20(B2:163-167).
DOI Link 2012
BibRef

Yuan, W.[Wei], Chen, S.[Shiyu], Zhang, Y.[Yong], Gong, J.Y.[Jian-Ya], Shibasaki, R.[Ryosuke],
An Aerial-image Dense Matching Approach Based On Optical Flow Field,
ISPRS16(B3: 543-548).
DOI Link 1610
BibRef

Zbontar, J.[Jure], Le Cun, Y.L.[Yann L.],
Computing the stereo matching cost with a convolutional neural network,
CVPR15(1592-1599)
IEEE DOI 1510
BibRef

Thomas, B., Yegnanarayana, B., Das, S.,
Stereo-correspondence using Gabor logons and neural networks,
ICIP95(II: 386-389).
IEEE DOI 9510
BibRef

Chapter on Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular continues in
Multi-Scale Matching for Stereo .


Last update:Oct 20, 2021 at 09:45:26