Representing actions with kernels

A long standing research goal is to create robots capable of interacting with humans in dynamic environments. To realise this a robot needs to understand and interpret the underlying meaning and intentions of a human action through a model of its sensory data. The visual domain provides a rich description of the environment and data is readily available in most system through inexpensive cameras. However, such data is very high-dimensional and extremely redundant making modeling challenging.

[1]  Babette Dellen,et al.  Disparity from Stereo-segment Silhouettes of Weakly-textured Images , 2009, BMVC.

[2]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[3]  Eren Erdal Aksoy,et al.  Segment Tracking via a Spatiotemporal Linking Process including Feedback Stabilization in an n-D Lattice Model , 2009, Sensors.

[4]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[5]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[6]  Sebastian Nowozin,et al.  Discriminative Subsequence Mining for Action Classification , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[7]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[8]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[9]  Jitendra Malik,et al.  From contours to regions: An empirical evaluation , 2009, CVPR.

[10]  Nello Cristianini,et al.  Classification using String Kernels , 2000 .

[11]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  Danica Kragic,et al.  Active 3D Segmentation through Fixation of Previously Unseen Objects , 2010, BMVC.

[13]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[14]  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).

[15]  Danica Kragic,et al.  Active 3D scene segmentation and detection of unknown objects , 2010, 2010 IEEE International Conference on Robotics and Automation.

[16]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[18]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[19]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[20]  J. Mercer.  Functions of positive and negative type, and their connection with the theory of integral equations , 1909 .

[21]  Eren Erdal Aksoy,et al.  Categorizing object-action relations from semantic scene graphs , 2010, 2010 IEEE International Conference on Robotics and Automation.

[22]  Cristian Sminchisescu,et al.  Semi-supervised Hierarchical Models for 3D Human Pose Reconstruction , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  John Shawe-Taylor,et al.  Syllables and other String Kernel Extensions , 2002, ICML.