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Visual system; Grasping movements; Motion analysis; Hand posture; Hand
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Hybrid Mapping for the Assistance of Teleoperated Grasping Tasks,
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Earlier:
Knowledge Representation and Inference for Grasp Affordances,
CVS11(173-182).
Springer DOI
1109
BibRef
And:
Surface reconstruction for RGB-D data using real-time depth propagation,
Dense11(723-724).
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Earlier:
Real-time depth diffusion for 3D surface reconstruction,
ICIP10(4149-4152).
IEEE DOI
1009
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Olufs, S.[Sven],
Vincze, M.[Markus],
Room-structure estimation in Manhattan-like environments from dense 2˝D
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Point Cloud Segmentation Based on Radial Reflection,
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Tracking hand rotation and various grasping gestures from an IR
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1310
BibRef
Earlier: A1, A3, Only:
Tracking Hand Rotation and Grasping from an IR Camera Using Cylindrical
Manifold Embedding,
ICPR10(2612-2615).
IEEE DOI
1008
Tracking
BibRef
Song, K.T.,
Jiang, S.Y.,
Lin, M.H.,
Interactive Teleoperation of a Mobile Manipulator Using a
Shared-Control Approach,
HMS(46), No. 6, December 2016, pp. 834-845.
IEEE DOI
1612
Grasping
BibRef
Cotugno, G.,
Althoefer, K.,
Nanayakkara, T.,
The Role of the Thumb: Study of Finger Motion in Grasping and
Reachability Space in Human and Robotic Hands,
SMCS(47), No. 7, July 2017, pp. 1061-1070.
IEEE DOI
1706
Grasping, Grippers, Kinematics, Robot kinematics, Standards, Thumb,
Dexterous manipulation, grasping, humanoid robots, kinematics,
multifingered, hands
BibRef
Cai, M.,
Kitani, K.M.,
Sato, Y.,
An Ego-Vision System for Hand Grasp Analysis,
HMS(47), No. 4, August 2017, pp. 524-535.
IEEE DOI
1708
Cameras, Feature extraction, Image segmentation, Robots, Sensors,
Taxonomy, Visualization, Egocentric vision, hand grasp, recognition,
wearable, system
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Song, P.[Peng],
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Grasp planning via hand-object geometric fitting,
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PAMI(40), No. 9, September 2018, pp. 2051-2065.
IEEE DOI
1808
BibRef
Earlier:
Model-Free Segmentation and Grasp Selection of Unknown Stacked Objects,
ECCV14(V: 659-674).
Springer DOI
1408
Labeling, Solid modeling, Proposals,
Semantics, Image reconstruction, Computational modeling,
semantic segmentation
BibRef
González-Díaz, I.[Iván],
Benois-Pineau, J.[Jenny],
Domenger, J.P.[Jean-Philippe],
Cattaert, D.[Daniel],
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Perceptually-guided deep neural networks for ego-action prediction:
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PR(88), 2019, pp. 223-235.
Elsevier DOI
1901
Human perception, Grasping action prediction,
Weakly supervised active object detection
BibRef
Chauhan, R.,
Sebastian, B.,
Ben-Tzvi, P.,
Grasp Prediction Toward Naturalistic Exoskeleton Glove Control,
HMS(50), No. 1, February 2020, pp. 22-31.
IEEE DOI
2001
Prediction algorithms, Exoskeletons,
Principal component analysis, Sensors, Electromyography,
upper limb
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2001
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Kemp, C.C.[Charles C.],
Hays, J.[James],
ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging,
CVPR19(8701-8711).
IEEE DOI
2002
BibRef
Tang, Q.[Qirong],
Hu, X.[Xue],
Chu, Z.[Zhugang],
Wu, S.[Shun],
6d Gripper Pose Estimation from RGB-D Image,
CVS19(120-125).
Springer DOI
1912
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Kiatos, M.[Marios],
Malassiotis, S.[Sotiris],
Grasping Unknown Objects by Exploiting Complementarity with Robot Hand
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1912
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Gritsenko, P.[Pavel],
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Kwolek, B.[Bogdan],
Plane-Based Humanoid Robot Navigation and Object Model Construction for
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4DPose18(I:649-664).
Springer DOI
1905
BibRef
Ghazaei, G.[Ghazal],
Laina, I.[Iro],
Rupprecht, C.[Christian],
Tombari, F.[Federico],
Navab, N.[Nassir],
Nazarpour, K.[Kianoush],
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions,
ACCV18(IV:38-55).
Springer DOI
1906
BibRef
Gupta, K.,
Burschka, D.,
Bhavsar, A.,
Effectiveness of Grasp Attributes and Motion-Constraints for
Fine-Grained Recognition of Object Manipulation Actions,
Hands16(1232-1239)
IEEE DOI
1612
BibRef
de Gregorio, D.[Daniele],
Tombari, F.[Federico],
di Stefano, L.[Luigi],
RobotFusion: Grasping with a Robotic Manipulator via Multi-view
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6DPose16(III: 634-647).
Springer DOI
1611
BibRef
Lakani, S.R.[Safoura Rezapour],
Rodríguez-Sánchez, A.J.[Antonio J.],
Piater, J.H.[Justus H.],
Can Affordances Guide Object Decomposition into Semantically
Meaningful Parts?,
WACV17(82-90)
IEEE DOI
1609
Computer vision, Grasping, Histograms, Semantics, Shape, Visualization
BibRef
Yang, Y.Z.[Ye-Zhou],
Fermuller, C.[Cornelia],
Li, Y.[Yi],
Aloimonos, Y.[Yiannis],
Grasp type revisited:
A modern perspective on a classical feature for vision,
CVPR15(400-408)
IEEE DOI
1510
BibRef
Huang, D.A.[De-An],
Ma, M.H.[Ming-Huang],
Ma, W.C.[Wei-Chiu],
Kitani, K.M.[Kris M.],
How do we use our hands? Discovering a diverse set of common grasps,
CVPR15(666-675)
IEEE DOI
1510
BibRef
Lakani, S.R.[Safoura Rezapour],
Popa, M.[Mirela],
Rodriguez-Sanchez, A.J.[Antonio J.],
Piater, J.H.[Justus H.],
CPS: 3D Compositional Part Segmentation through Grasping,
CRV15(117-124)
IEEE DOI
1507
Feature extraction
BibRef
Kim, J.W.,
You, S.,
Ji, S.H.,
Kim, H.S.,
Real-Time Hand Grasp Recognition Using Weakly Supervised Two-Stage
Convolutional Neural Networks for Understanding Manipulation Actions,
DeepLearnRV17(481-483)
IEEE DOI
1709
Feature extraction, Grasping, Image recognition,
Network architecture, Pattern recognition, Real-time, systems
BibRef
Thonnagith, P.,
Sudsang, A.,
New hand posture classification strategy for finding
kinematically-feasible precision grasps,
ICARCV10(1583-1588).
IEEE DOI
1109
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Wörsdörfer, F.[Florian],
Stock, F.[Florian],
Bayro-Corrochano, E.[Eduardo],
Hildenbrand, D.[Dietmar],
Optimizations and Performance of a Robotics Grasping Algorithm
Described in Geometric Algebra,
CIARP09(263-271).
Springer DOI
0911
BibRef
Yasumuro, Y.[Yoshihiro],
Yamazaki, M.[Masayuki],
Imura, M.[Masataka],
Manabe, Y.[Yoshitsugu],
Chihara, K.[Kunihiro],
Grasp Motion Synthesis Based on Object Features,
AMDO06(305-314).
Springer DOI
0607
BibRef
Taylor, M.J.,
Blake, A.,
Grasping the Apparent Contour,
ECCV94(B:25-34).
Springer DOI
BibRef
9400
Blake, A.,
Taylor, M.J., and
Cox, A.,
Grasping Visual Symmetry,
ICCV93(724-733).
IEEE DOI Symmetry as the locus of bi-tangent circles.
BibRef
9300
Gatrell, L.B.[Lance B.],
CAD-Based Grasp Synthesis Utilizing Polygons, Edges, and Vertices,
CRA89(184-189).
BibRef
8900
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Carried Objects, Carrying Objects .