ITCVPR11
* *Workshop in Information Theory in Computer Vision and Pattern Recognition
* Bayesian online learning on Riemannian manifolds using a dual model with applications to video object tracking
* Computing importance of 2D contour parts by reconstructability
* Efficient variational inference in large-scale Bayesian compressed sensing
* Evaluating image segments by applying the description length to sets of superpixels
* Exploring the representation capabilities of the HOG descriptor
* information geometry approach to shape density Minimum Description Length model selection, An
* information theoretic approach to gender feature selection, An
* Information theoretic preattentive saliency: A closed-form solution
* invertible dimension reduction of curves on a manifold, A
* Iteratively merging information from a pair of flash/no-flash images using nonlinear diffusion
* multi-affine model for tensor decomposition, A
* Probabilistic shape-based segmentation using level sets
* Search pruning in video surveillance systems: Efficiency-reliability tradeoff
* STARS: A new ensemble partitioning approach
* Supervised feature quantization with entropy optimization
* Tensor-based total Bregman divergences between graphs
* Towards a theory of compositional learning and encoding of objects
18 for ITCVPR11