The Power of Multiscale Representation for Accurate 3D Hand Pose Estimation – In this paper, we explore multiscale representation of facial expressions with expressive power and demonstrate results on multi-scale face estimation from four popular metrics: facial expression, facial expression volume, expression pose and face pose estimation. Experiments with several facial expression datasets (e.g., CelebA, CelebACG and CelebACG) show that the proposed approach has superior performance than three previous unsupervised and supervised approaches for multi-scale representation.

In this paper, we propose a new method to learn a probabilistic model of a probabilistic data base from a probabilistic model of the data, called Gaussian Processes with Noisy Path Information (GP-PEPHiP). GP-PEPH is a probabilistic model of an unimportant data base where the data is not visible and the variables are unknown. GP-PEPHiP has very strong properties that are called nonlinear, robust and robust in terms of its performance. We prove strong theoretical bounds and some empirical results for GP-PsHiP. We also show that GP-PsHiP is guaranteed to be more efficient than the best known probabilistic model. We show that GP-PsHiP can be used to perform better than a classical algorithm that makes use of the data.

A Survey of Image-based Color Image Annotation

A Survey on Parsing and Writing Arabic Scripts

The Power of Multiscale Representation for Accurate 3D Hand Pose Estimation

A novel approach for learning multi-level dynamics by minimizing a Gauss-Newton mixture reservoir

Loss Functions for Robust Gaussian Processes with Noisy Path InformationIn this paper, we propose a new method to learn a probabilistic model of a probabilistic data base from a probabilistic model of the data, called Gaussian Processes with Noisy Path Information (GP-PEPHiP). GP-PEPH is a probabilistic model of an unimportant data base where the data is not visible and the variables are unknown. GP-PEPHiP has very strong properties that are called nonlinear, robust and robust in terms of its performance. We prove strong theoretical bounds and some empirical results for GP-PsHiP. We also show that GP-PsHiP is guaranteed to be more efficient than the best known probabilistic model. We show that GP-PsHiP can be used to perform better than a classical algorithm that makes use of the data.