21.4.2.3.3 Depth Based, Stereo, Hand Pose, Hand Posture, Hand Shape

Chapter Contents (Back)
Hand Pose. Hand Posture. Depth.
See also Hand Pose, Hand Posture, Hand Shape.

Liang, H.[Hui], Yuan, J.S.[Jun-Song], Thalmann, D.,
Parsing the Hand in Depth Images,
MultMed(16), No. 5, August 2014, pp. 1241-1253.
IEEE DOI 1410
Markov processes BibRef

Ge, L.H.[Liu-Hao], Liang, H.[Hui], Yuan, J.S.[Jun-Song], Thalmann, D.[Daniel],
Robust 3D Hand Pose Estimation From Single Depth Images Using Multi-View CNNs,
IP(27), No. 9, September 2018, pp. 4422-4436.
IEEE DOI 1807
BibRef
Earlier:
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation from Single Depth Images,
CVPR17(5679-5688)
IEEE DOI 1711
BibRef
Earlier:
Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs,
CVPR16(3593-3601)
IEEE DOI 1612
neural nets, pose estimation, probability, regression analysis, articulated hand pose estimation, depth information, multi-view CNNs. Feature extraction, Real-time systems, Robustness, Solid modeling, BibRef

Ge, L.H.[Liu-Hao], Liang, H.[Hui], Yuan, J.S.[Jun-Song], Thalmann, D.[Daniel],
Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks,
PAMI(41), No. 4, April 2019, pp. 956-970.
IEEE DOI 1903
Pose estimation, Feature extraction, Solid modeling, deep learning BibRef

Xu, C.[Chi], Nanjappa, A.[Ashwin], Zhang, X.W.[Xiao-Wei], Cheng, L.[Li],
Estimate Hand Poses Efficiently from Single Depth Images,
IJCV(116), No. 1, January 2016, pp. 21-45.
Springer DOI 1601
BibRef

Xu, C.[Chi], Govindarajan, L.N.[Lakshmi Narasimhan], Zhang, Y.[Yu], Stewart, J.[James], Bichler, Z.[Zoë], Jesuthasan, S.[Suresh], Claridge-Chang, A.[Adam], Mathuru, A.S.[Ajay Sriram], Tang, W.L.[Wen-Long], Zhu, P.[Peixin], Cheng, L.[Li],
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups,
IJCV(123), No. 3, July 2017, pp. 454-478.
Springer DOI 1706
BibRef
And: Author List Corrected: IJCV(126), No. 8, August 2018, pp. 897.
Springer DOI 1807
BibRef

Supancic, III, J.S.[James Steven], Rogez, G.[Grégory], Yang, Y.[Yi], Shotton, J.[Jamie], Ramanan, D.[Deva],
Depth-Based Hand Pose Estimation: Methods, Data, and Challenges,
IJCV(126), No. 11, November 2018, pp. 1180-1198.
Springer DOI 1809
BibRef
Earlier:
Depth-Based Hand Pose Estimation: Data, Methods, and Challenges,
ICCV15(1868-1876)
IEEE DOI 1602
BibRef
Earlier: A2, A1, A5, Only:
Understanding Everyday Hands in Action from RGB-D Images,
ICCV15(3889-3897)
IEEE DOI 1602
Benchmark testing; Clutter; Data models; Training data. Cameras BibRef

Basaru, R.R.[Rilwan Remilekun], Child, C.[Chris], Alonso, E.[Eduardo], Slabaugh, G.[Gregory],
Data-driven recovery of hand depth using CRRF on stereo images,
IET-CV(12), No. 5, August 2018, pp. 666-678.
DOI Link 1807
BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Gao, Z.M.[Zhi-Min], Tang, C.[Chang], Ogunbona, P.O.[Philip O.],
Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks,
MultMed(20), No. 5, May 2018, pp. 1051-1061.
IEEE DOI 1805
Dynamics, Feature extraction, Gesture recognition, Image recognition, Image segmentation, Motion segmentation, depth BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Liu, S.[Song], Zhang, Y.Y.[Yu-Yao], Gao, Z.M.[Zhi-Min], Ogunbona, P.O.[Philip O.],
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks,
ICPR16(13-18)
IEEE DOI 1705
Feature extraction, Gesture recognition, Image segmentation, Motion segmentation, Neural networks, Training, Transforms, convolutional neural network, depth map sequence, depth motion map, gesture, recognition
See also Large-Scale Multimodal Gesture Recognition Using Heterogeneous Networks. BibRef

Li, C.K.[Chuan-Kun], Hou, Y.H.[Yong-Hong], Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing],
Joint Distance Maps Based Action Recognition With Convolutional Neural Networks,
SPLetters(24), No. 5, May 2017, pp. 624-628.
IEEE DOI 1704
image colour analysis BibRef

Li, C.K.[Chuan-Kun], Hou, Y.H.[Yong-Hong], Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing],
Multiview-Based 3-D Action Recognition Using Deep Networks,
HMS(49), No. 1, February 2019, pp. 95-104.
IEEE DOI 1901
Skeleton, Trajectory, Feature extraction, Recurrent neural networks, Image color analysis, Encoding, three dimensional (3-D) BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Gao, Z.M.[Zhi-Min], Zhang, Y.Y.[Yu-Yao], Tang, C.[Chang], Ogunbona, P.O.[Philip O.],
Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks,
CVPR17(416-425)
IEEE DOI 1711
Cameras, Feature extraction, Kernel, Optical imaging, Transforms, Videos BibRef

Zhang, J.[Jing], Li, W.Q.[Wan-Qing], Wang, P.C.[Pi-Chao], Ogunbona, P.[Philip], Liu, S.[Song], Tang, C.[Chang],
A Large Scale RGB-D Dataset for Action Recognition,
UHA3DS16(101-114).
Springer DOI 1806
BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Liu, S.[Song], Gao, Z.M.[Zhi-Min], Tang, C.[Chang], Ogunbona, P.O.[Philip O.],
Large-Scale Isolated Gesture Recognition Using Convolutional Neural Networks,
ICPR16(7-12)
IEEE DOI 1705
Convolution, Dynamics, Feature extraction, Gesture recognition, Neural networks, Testing, Training, Convolutional Neural Networks, depth map sequences, gesture, recognition BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Gao, Z.M.[Zhi-Min], Zhang, J.[Jing], Tang, C.[Chang], Ogunbona, P.O.[Philip O.],
Action Recognition From Depth Maps Using Deep Convolutional Neural Networks,
HMS(46), No. 4, August 2016, pp. 498-509.
IEEE DOI 1608
data mining BibRef

Hou, Y.H.[Yong-Hong], Li, Z.Y.[Zhao-Yang], Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing],
Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks,
CirSysVideo(28), No. 3, March 2018, pp. 807-811.
IEEE DOI 1804
convolution, feature extraction, feedforward neural nets, image coding, image colour analysis, image motion analysis, skeleton BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Ogunbona, P.O.[Philip O.], Gao, Z.M.[Zhi-Min], Zhang, H.L.[Han-Ling],
Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation,
DICTA14(1-8)
IEEE DOI 1502
computer graphics BibRef

Gomez-Donoso, F.[Francisco], Orts-Escolano, S.[Sergio], Cazorla, M.[Miguel],
Large-scale multiview 3D hand pose dataset,
IVC(81), 2019, pp. 25-33.
Elsevier DOI 1902
3D hand pose, Multiview, Dataset, Deep learning BibRef

Wang, G.J.[Gui-Jin], Chen, X.H.[Xing-Hao], Guo, H.K.[Heng-Kai], Zhang, C.R.[Cai-Rong],
Region ensemble network: Towards good practices for deep 3D hand pose estimation,
JVCIR(55), 2018, pp. 404-414.
Elsevier DOI 1809
Convolutional network, Hand pose estimation, Human pose estimation, Fingertip detection, Ensemble learning, Depth imaging BibRef

Guo, H., Wang, G., Chen, X., Zhang, C., Qiao, F., Yang, H.,
Region ensemble network: Improving convolutional network for hand pose estimation,
ICIP17(4512-4516)
IEEE DOI 1803
Bagging, Convolution, Pose estimation, Testing, Training, Convolutional Network, Hand Pose Estimation BibRef

Wu, Y., Ji, W., Li, X., Wang, G., Yin, J., Wu, F.,
Context-Aware Deep Spatiotemporal Network for Hand Pose Estimation From Depth Images,
Cyber(50), No. 2, February 2020, pp. 787-797.
IEEE DOI 1912
Feature extraction, Pose estimation, Spatiotemporal phenomena, Image sequences, Context modeling, Adaptation models, Data mining, hand pose estimation BibRef

Oberweger, M.[Markus], Wohlhart, P.[Paul], Lepetit, V.[Vincent],
Generalized Feedback Loop for Joint Hand-Object Pose Estimation,
PAMI(42), No. 8, August 2020, pp. 1898-1912.
IEEE DOI 2007
BibRef
Earlier:
Training a Feedback Loop for Hand Pose Estimation,
ICCV15(3316-3324)
IEEE DOI 1602
Pose estimation, Solid modeling, Optimization, Feedback loop, Training data, Data models, hand-object manipulation BibRef

Oberweger, M., Riegler, G., Wohlhart, P., Lepetit, V.,
Efficiently Creating 3D Training Data for Fine Hand Pose Estimation,
CVPR16(4957-4965)
IEEE DOI 1612
Data models BibRef

Wang, L., Cao, Z., Cui, Z., Cao, C., Pi, Y.,
Negative Latency Recognition Method for Fine-Grained Gestures Based on Terahertz Radar,
GeoRS(58), No. 11, November 2020, pp. 7955-7968.
IEEE DOI 2011
Gesture recognition, Doppler radar, Radar imaging, Indexes, Human computer interaction, Acoustics, Gesture recognition, terahertz radar BibRef

Yang, J.[Jian], Ma, X.H.[Xiao-Hong], Sun, Y.[Yi], Lin, X.B.[Xiang-Bo],
LPPM-Net: Local-aware point processing module based 3D hand pose estimation for point cloud,
SP:IC(90), 2021, pp. 116036.
Elsevier DOI 2012
Hand pose estimation, Point cloud, Deep learning, Local-aware point processing module BibRef

Deng, X., Zhu, Y., Zhang, Y., Cui, Z., Tan, P., Qu, W., Ma, C., Wang, H.,
Weakly Supervised Learning for Single Depth-Based Hand Shape Recovery,
IP(30), 2021, pp. 532-545.
IEEE DOI 2012
Shape, Skeleton, Solid modeling, Image reconstruction, Pose estimation, Data models, gesture recognition BibRef

Zhang, Y.[Yu], Mi, S.[Siya], Wu, J.X.[Jian-Xin], Geng, X.[Xin],
Simultaneous 3D hand detection and pose estimation using single depth images,
PRL(140), 2020, pp. 43-48.
Elsevier DOI 2012
3D hand pose estimation, Hand detection, 3D region proposal BibRef

Sun, J.[Jin], Zhang, Z.[Zhe], Yang, L.[Liutao], Zheng, J.P.[Ji-Ping],
Multi-view hand gesture recognition via pareto optimal front,
IET-IPR(14), No. 14, December 2020, pp. 3579-3587.
DOI Link 2012
BibRef

Xu, L., Hu, C., Tao, J., Xue, J., Mei, K.,
Improve Regression Network on Depth Hand Pose Estimation With Auxiliary Variable,
CirSysVideo(31), No. 3, March 2021, pp. 890-904.
IEEE DOI 2103
Pose estimation, Feature extraction, Kinematics, Task analysis, Training, variational autoencoder BibRef

Kumar, A.K.[Amit Krishan], Kumar, A.K.[Abhishek Kaushal], Guo, S.[Shuli],
Two viewpoints based real-time recognition for hand gestures,
IET-IPR(14), No. 17, 24 December 2020, pp. 4606-4613.
DOI Link 2104
BibRef

Xia, Z.Y.[Zhao-Yang], Luomei, Y.[Yixiang], Zhou, C.L.[Cheng-Long], Xu, F.[Feng],
Multidimensional Feature Representation and Learning for Robust Hand-Gesture Recognition on Commercial Millimeter-Wave Radar,
GeoRS(59), No. 6, June 2021, pp. 4749-4764.
IEEE DOI 2106
Gesture recognition, Feature extraction, Robustness, Radar imaging, Signal resolution, Scattering, Detection and tracking, multidimensional feature BibRef


Chen, L.J.[Liang-Jian], Lin, S.Y.[Shih-Yao], Xie, Y.S.[Yu-Sheng], Lin, Y.Y.[Yen-Yu], Xie, X.H.[Xiao-Hui],
MVHM: A Large-Scale Multi-View Hand Mesh Benchmark for Accurate 3D Hand Pose Estimation,
WACV21(836-845)
IEEE DOI 2106
Training, Annotations, Fuses, Pose estimation, Pipelines BibRef

Chen, L.J.[Liang-Jian], Lin, S.Y.[Shih-Yao], Xie, Y.S.[Yu-Sheng], Lin, Y.Y.[Yen-Yu], Xie, X.H.[Xiao-Hui],
Temporal-Aware Self-Supervised Learning for 3D Hand Pose and Mesh Estimation in Videos,
WACV21(1049-1058)
IEEE DOI 2106
Training, Solid modeling, Annotations, Computational modeling, BibRef

Caramalau, R.[Razvan], Bhattarai, B.[Binod], Kim, T.K.[Tae-Kyun],
Active Learning for Bayesian 3D Hand Pose Estimation,
WACV21(3418-3427)
IEEE DOI 2106
Deep learning, Uncertainty, Pose estimation, Redundancy BibRef

Kok, F.[Felix], Charles, J.[James], Cipolla, R.[Roberto],
Footnet: An Efficient Convolutional Network for Multiview 3d Foot Reconstruction,
ACCV20(VI:36-51).
Springer DOI 2103
BibRef

Jaswal, G.[Gaurav], Srirangarajan, S.[Seshan], Roy, S.D.[Sumantra Dutta],
Range-doppler Hand Gesture Recognition Using Deep Residual-3dcnn with Transformer Network,
MPRSS20(759-772).
Springer DOI 2103
BibRef

Wang, J., Mueller, F., Bernard, F., Theobalt, C.,
Generative Model-Based Loss to the Rescue: A Method to Overcome Annotation Errors for Depth-Based Hand Pose Estimation,
FG20(101-108)
IEEE DOI 2102
Gesture recognition, Face recognition, hand pose estimation, annotation bias, generative loss, depth image BibRef

Fang, L.P.[Lin-Pu], Liu, X.Y.[Xing-Yan], Liu, L.[Li], Xu, H.[Hang], Kang, W.X.[Wen-Xiong],
JGR-P2O: Joint Graph Reasoning Based Pixel-to-offset Prediction Network for 3d Hand Pose Estimation from a Single Depth Image,
ECCV20(VI:120-137).
Springer DOI 2011
BibRef

Yang, J.[John], Chang, H.J.[Hyung Jin], Lee, S.[Seungeui], Kwak, N.J.[No-Jun],
Seqhand: RGB-sequence-based 3d Hand Pose and Shape Estimation,
ECCV20(XII: 122-139).
Springer DOI 2010
BibRef

Wan, C.D.[Cheng-De], Probst, T.[Thomas], Van Gool, L.J.[Luc J.], Yao, A.[Angela],
Dual Grid Net: Hand Mesh Vertex Regression from Single Depth Maps,
ECCV20(XXX: 442-459).
Springer DOI 2010
BibRef

Min, Y., Zhang, Y., Chai, X., Chen, X.,
An Efficient PointLSTM for Point Clouds Based Gesture Recognition,
CVPR20(5760-5769)
IEEE DOI 2008
Gesture recognition, Feature extraction, Task analysis, Data mining, Logic gates, Computer vision BibRef

Gu, J., Wang, Z., Ouyang, W., Zhang, W., Li, J., Zhuo, L.,
3D Hand Pose Estimation with Disentangled Cross-Modal Latent Space,
WACV20(380-389)
IEEE DOI 2006
Pose estimation, Task analysis, Solid modeling, Decoding BibRef

Chen, L., Lin, S., Xie, Y., Lin, Y., Fan, W., Xie, X.,
DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation,
WACV20(400-408)
IEEE DOI 2006
Training, Image reconstruction, Pose estimation, Task analysis, Solid modeling BibRef

Zhu, T., Sun, Y., Ma, X., Lin, X.,
Hand Pose Ensemble Learning Based on Grouping Features of Hand Point Sets,
Hands19(2856-2865)
IEEE DOI 2004
feature extraction, feature selection, learning (artificial intelligence), pose estimation, Ensemble learning BibRef

Ge, L., Cai, Y., Weng, J., Yuan, J.,
Hand PointNet: 3D Hand Pose Estimation Using Point Sets,
CVPR18(8417-8426)
IEEE DOI 1812
Pose estimation, Feature extraction, Cameras, Neural networks, Sensitivity BibRef

Malik, J., Elhayek, A., Nunnari, F., Varanasi, K., Tamaddon, K., Heloir, A., Stricker, D.,
DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth,
3DV18(110-119)
IEEE DOI 1812
computer vision, image classification, image motion analysis, image segmentation, learning (artificial intelligence), convolutional neural networks BibRef

Baek, S., Kim, K.I., Kim, T.,
Augmented Skeleton Space Transfer for Depth-Based Hand Pose Estimation,
CVPR18(8330-8339)
IEEE DOI 1812
Skeleton, Shape, Training, Cameras, Pose estimation, Databases BibRef

Li, J., Wang, Z.,
Local Regression Based Hourglass Network for Hand Pose Estimation from a Single Depth Image,
ICPR18(1767-1772)
IEEE DOI 1812
Feature extraction, Task analysis, Pose estimation, Convolutional neural networks, Sensors BibRef

Yuan, S., Garcia-Hernando, G., Stenger, B., Moon, G., Chang, J.Y., Lee, K.M., Molchanov, P., Kautz, J., Honari, S., Ge, L., Yuan, J., Chen, X., Wang, G., Yang, F., Akiyama, K., Wu, Y., Wan, Q., Madadi, M., Escalera, S., Li, S., Lee, D., Oikonomidis, I., Argyros, A., Kim, T.,
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals,
CVPR18(2636-2645)
IEEE DOI 1812
Task analysis, Pose estimation, Joints, Training, Solid modeling BibRef

Wan, C., Probst, T., Van Gool, L.J., Yao, A.,
Dense 3D Regression for Hand Pose Estimation,
CVPR18(5147-5156)
IEEE DOI 1812
Pose estimation, Heating systems, Skeleton, Correlation BibRef

Zhou, Y.[Yidan], Lu, J.[Jian], Du, K.[Kuo], Lin, X.B.[Xiang-Bo], Sun, Y.[Yi], Ma, X.H.[Xiao-Hong],
HBE: Hand Branch Ensemble Network for Real-Time 3D Hand Pose Estimation,
ECCV18(XIV: 521-536).
Springer DOI 1810
BibRef

Armagan, A.[Anil], Garcia-Hernando, G.[Guillermo], Baek, S.[Seungryul], Hampali, S.[Shreyas], Rad, M.[Mahdi], Zhang, Z.H.[Zhao-Hui], Xie, S.P.[Shi-Peng], Chen, M.X.[Ming-Xiu], Zhang, B.[Boshen], Xiong, F.[Fu], Xiao, Y.[Yang], Cao, Z.G.[Zhi-Guo], Yuan, J.S.[Jun-Song], Ren, P.F.[Peng-Fei], Huang, W.T.[Wei-Ting], Sun, H.F.[Hai-Feng], Hrúz, M.[Marek], Kanis, J.[Jakub], Krnoul, Z.[Zdenek], Wan, Q.F.[Qing-Fu], Li, S.[Shile], Yang, L.L.[Lin-Lin], Lee, D.H.[Dong-Heui], Yao, A.[Angela], Zhou, W.G.[Wei-Guo], Mei, S.[Sijia], Liu, Y.[Yunhui], Spurr, A.[Adrian], Iqbal, U.[Umar], Molchanov, P.[Pavlo], Weinzaepfel, P.[Philippe], Brégier, R.[Romain], Rogez, G.[Grégory], Lepetit, V.[Vincent], Kim, T.K.[Tae-Kyun],
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3d Hand Pose Estimation Under Hand-object Interaction,
ECCV20(XXIII:85-101).
Springer DOI 2011
BibRef

Ge, L.H.[Liu-Hao], Ren, Z.[Zhou], Yuan, J.S.[Jun-Song],
Point-to-Point Regression PointNet for 3D Hand Pose Estimation,
ECCV18(XIII: 489-505).
Springer DOI 1810
BibRef

Zhang, J., Jiao, J., Chen, M., Qu, L., Xu, X., Yang, Q.,
A hand pose tracking benchmark from stereo matching,
ICIP17(982-986)
IEEE DOI 1803
Benchmark testing, Cameras, Image color analysis, Image segmentation, Noise measurement, Skin, Training, stereo matching and hand pose tracking BibRef

Basaru, R.R., Child, C., Alonso, E., Slabaugh, G.,
Hand Pose Estimation Using Deep Stereovision and Markov-Chain Monte Carlo,
Hands17(595-603)
IEEE DOI 1802
Cameras, Monte Carlo methods, Pose estimation, Proposals, Robustness BibRef

Otberdout, N., Ballihi, L., Aboutajdine, D.,
Hand pose estimation based on deep learning depth map for hand gesture recognition,
ISCV17(1-8)
IEEE DOI 1710
Gesture recognition, Neural networks, Neurons, Pose estimation, Solid modeling, Three-dimensional, displays BibRef

Goudie, D., Galata, A.,
3D Hand-Object Pose Estimation from Depth with Convolutional Neural Networks,
FG17(406-413)
IEEE DOI 1707
Cost function, Image segmentation, Neural networks, Pose estimation, Sensors, Training BibRef

Madadi, M., Escalera, S., Carruesco, A., Andujar, C., Baró, X., Gonzàlez, J.,
Occlusion Aware Hand Pose Recovery from Sequences of Depth Images,
FG17(230-237)
IEEE DOI 1707
Cameras, Computational modeling, Feature extraction, Pose estimation, Shape, Solid modeling, Three-dimensional, displays BibRef

Asad, M.[Muhammad], Gentet, E.[Enguerrand], Basaru, R.R.[Rilwan Remilekun], Slabaugh, G.[Greg],
Generatinga 3D hand model from frontal color and range scans,
ICIP15(4589-4593)
IEEE DOI 1512
Hand model; pose estimation; reconstruction BibRef

Guo, H., Wang, G., Chen, X.,
Two-stream convolutional neural network for accurate RGB-D fingertip detection using depth and edge information,
ICIP16(2608-2612)
IEEE DOI 1610
BibRef
And: A3, A2, A1:
Accurate fingertip detection from binocular mask images,
VCIP16(1-4)
IEEE DOI 1701
Image edge detection BibRef

Khamis, S.[Sameh], Taylor, J.[Jonathan], Shotton, J.[Jamie], Keskin, C.[Cem], Izadi, S.[Shahram], Fitzgibbon, A.W.[Andrew W.],
Learning an efficient model of hand shape variation from depth images,
CVPR15(2540-2548)
IEEE DOI 1510
BibRef

Fan, C.Y.[Chin-Yun], Lin, M.H.[Meng-Hsuan], Su, T.F.[Te-Feng], Lai, S.H.[Shang-Hong], Yu, C.H.[Chih-Hsiang],
3D hand skeleton model estimation from a depth image,
MVA15(489-492)
IEEE DOI 1507
Computational modeling BibRef

Sohn, M.K.[Myoung-Kyu], Kim, D.J.[Dong-Ju], Kim, H.[Hyunduk],
Hand Part Classification Using Single Depth Images,
UCCV14(253-261).
Springer DOI 1504
BibRef

Rogez, G.[Grégory], Khademi, M.[Maryam], Supancic, III, J.S., Montiel, J.M.M., Ramanan, D.[Deva],
3D Hand Pose Detection in Egocentric RGB-D Images,
CDC4CV14(356-371).
Springer DOI 1504
BibRef

Taylor, J.[Jonathan], Stebbing, R.[Richard], Ramakrishna, V.[Varun], Keskin, C.[Cem], Shotton, J.[Jamie], Izadi, S.[Shahram], Hertzmann, A.[Aaron], Fitzgibbon, A.W.[Andrew W.],
User-Specific Hand Modeling from Monocular Depth Sequences,
CVPR14(644-651)
IEEE DOI 1409
BibRef

Kang, B.[Byungkyu], Rodrigue, M., Hollerer, T., Lim, H.[Hwasup],
Real time hand pose recognition with depth sensors for mixed reality interfaces,
3DUI13(171-172)
IEEE DOI 1406
decision trees BibRef

Xu, C.[Chi], Cheng, L.[Li],
Efficient Hand Pose Estimation from a Single Depth Image,
ICCV13(3456-3462)
IEEE DOI 1403
hand pose estimation; random forest; realtime BibRef

Bagdanov, A.D.[Andrew D.], del Bimbo, A.[Alberto], Seidenari, L.[Lorenzo], Usai, L.[Lorenzo],
Real-time hand status recognition from RGB-D imagery,
ICPR12(2456-2459).
WWW Link. 1302
BibRef

Wang, Q.[Qi], Chen, X.L.[Xi-Lin], Gao, W.[Wen],
Skin Color Weighted Disparity Competition for Hand Segmentation from Stereo Camera,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Tran, C.[Cuong], Trivedi, M.M.[Mohan M.],
Hand modeling and tracking from voxel data: An integrated framework with automatic initialization,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Delamarre, Q., Faugeras, O.D.,
Finding Pose of Hand in Video Images: A Stereo-Based Approach,
AFGR98(585-590).
IEEE DOI
PS File. BibRef 9800

Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Hand Tracking for Gestures .


Last update:Nov 30, 2021 at 22:19:38