Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
The wavelet transform hierarchically decomposes images with prescribed bases, while multilineal models search for optimal bases to adapt visual data. In this paper, we integrate t...
This paper describes a probabilistic method of aligning and merging range images. We formulate these issues as problems of estimating the maximum likelihood. By examining the erro...
Principal component analyses (PCA) has been widely used in reduction of the dimensionality of datasets, classification, feature extraction, etc. It has been combined with many oth...
We present an approach for recovering articulated body pose from single monocular images using the Specialized Mappings Architecture (SMA), a non-linear supervised learning archit...