Learning Generative Models for Multi-Activity Body Pose Estimation
International Journal of Computer Vision
Factor Analysis of Incidence Data via Novel Decomposition of Matrices
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Discovery of optimal factors in binary data via a novel method of matrix decomposition
Journal of Computer and System Sciences
Learning generative models for monocular body pose estimation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Multi-activity tracking in LLE body pose space
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Gait recognition based on improved dynamic Bayesian networks
Pattern Recognition
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There are various situations where image data is binary: character recognition, result of image segmentation etc. As a first contribution, we compare Gaussian based principal component analysis (PCA), which is often used to model images, and "binary PCA" which models the binary data more naturally using Bernoulli distributions. Furthermore, we address the problem of data alignment. Image data is often perturbed by some global transformations such as shifting, rotation, scaling etc. In such cases the data needs to be transformed to some canonical aligned form. As a second contribution, we extend the binary PCA to the "transformation invariant mixture of binary PCAs" which simultaneously corrects the data for a set of global transformations and learns the binary PCA model on the aligned data.