Visual object-action recognition: Inferring object affordances from human demonstration
暂无分享,去创建一个
[1] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[2] Danica Kragic,et al. Hands in action: real-time 3D reconstruction of hands in interaction with objects , 2010, 2010 IEEE International Conference on Robotics and Automation.
[3] Larry S. Davis,et al. Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Danica Kragic,et al. Grasping familiar objects using shape context , 2009, 2009 International Conference on Advanced Robotics.
[5] C. Schmid,et al. Actions in context , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[6] Florentin Wörgötter,et al. Cognitive agents - a procedural perspective relying on the predictability of Object-Action-Complexes (OACs) , 2009, Robotics Auton. Syst..
[7] Henk Nijmeijer,et al. Robot Programming by Demonstration , 2010, SIMPAR.
[8] Stefan Schaal,et al. Robot Programming by Demonstration , 2009, Springer Handbook of Robotics.
[9] Larry S. Davis,et al. Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers , 2008, ECCV.
[10] Danica Kragic,et al. Simultaneous Visual Recognition of Manipulation Actions and Manipulated Objects , 2008, ECCV.
[11] Nick Pears. RBF shape histograms and their application to 3D face processing , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.
[12] Roman Filipovych,et al. Recognizing primitive interactions by exploring actor-object states , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[13] Bernt Schiele,et al. Functional Object Class Detection Based on Learned Affordance Cues , 2008, ICVS.
[14] Ashutosh Saxena,et al. Robotic Grasping of Novel Objects using Vision , 2008, Int. J. Robotics Res..
[15] Manuel Lopes,et al. Learning Object Affordances: From Sensory--Motor Coordination to Imitation , 2008, IEEE Transactions on Robotics.
[16] James M. Rehg,et al. A Scalable Approach to Activity Recognition based on Object Use , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[17] Fei-Fei Li,et al. What, where and who? Classifying events by scene and object recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[18] Trevor Darrell,et al. Learning Visual Representations using Images with Captions , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[19] Christopher W. Geib,et al. The meaning of action: a review on action recognition and mapping , 2007, Adv. Robotics.
[20] Danica Kragic,et al. Action recognition and understanding through motor primitives , 2007, Adv. Robotics.
[21] Andrew McCallum,et al. Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..
[22] Adrian Hilton,et al. A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..
[23] Irfan A. Essa,et al. Learning Temporal Sequence Model from Partially Labeled Data , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[24] Cristian Sminchisescu,et al. Conditional models for contextual human motion recognition , 2006, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[25] Manuela M. Veloso,et al. Learning visual object definitions by observing human activities , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..
[26] Svetha Venkatesh,et al. Combining image regions and human activity for indirect object recognition in indoor wide-angle views , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[27] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[28] Rüdiger Dillmann,et al. Towards Cognitive Robots: Building Hierarchical Task Representations of Manipulations from Human Demonstration , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.
[29] David G. Stork,et al. Generic object recognition using form and function , 1998, Pattern Analysis and Applications.
[30] Antonio Torralba,et al. Contextual Models for Object Detection Using Boosted Random Fields , 2004, NIPS.
[31] Y. LeCun,et al. Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[32] Nando de Freitas,et al. A Statistical Model for General Contextual Object Recognition , 2004, ECCV.
[33] Antonio Torralba,et al. Contextual Priming for Object Detection , 2003, International Journal of Computer Vision.
[34] Antonio Torralba,et al. Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes , 2003, NIPS.
[35] Giulio Sandini,et al. Learning about objects through action - initial steps towards artificial cognition , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).
[36] Trevor Darrell,et al. Fast pose estimation with parameter-sensitive hashing , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[37] D. Ballard,et al. What you see is what you need. , 2003, Journal of vision.
[38] Dustin Boswell,et al. Introduction to Support Vector Machines , 2002 .
[39] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[40] Refractor. Vision , 2000, The Lancet.
[41] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[42] Stefan Schaal,et al. Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.
[43] Irfan A. Essa,et al. Exploiting human actions and object context for recognition tasks , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[44] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.
[45] D. Stork. Generic object recognition using form & function , 1996 .
[46] Azriel Rosenfeld,et al. Recognition by Functional Parts , 1995, Comput. Vis. Image Underst..
[47] Katsushi Ikeuchi,et al. Task-oriented generation of visual sensing strategies , 1995, Proceedings of IEEE International Conference on Computer Vision.
[48] Katsushi Ikeuchi,et al. Task Oriented Vision , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.
[49] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[50] J. Gibson. The Ecological Approach to Visual Perception , 1979 .