@inproceedings{ZivkVerb2006CVPR,
authors="Z.Zivkovic and J.J. Verbeek",
title ="Transformation invariant component analysis for binary images",
booktitle="In Proc.IEEE Conference on Computer Vision and Pattern Recognition",
pages="254-259",
year=2006,
abstract="There are various situations where image data is binary:
character recognition, result of image segmentation etc. As
a rst 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."
}