University of Cambridge > Talks.cam > Microsoft Research Machine Learning and Perception Seminars > A complete set of rotationally and translationally invariant features based on a generalization of the bispectrum to non-commutative groups

A complete set of rotationally and translationally invariant features based on a generalization of the bispectrum to non-commutative groups

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Oliver Williams.

Deriving translation and rotation invariant representations is a fundamental problem in computer vision with a substantial literature. I propose a new set of features which

a, are simultaneously invariant to translation and rotation; b, are sufficient to reconstruct the original image with no loss (up to a badwidth limit); c, do not involve matching with a template image or any similar discontinuous operation.

The new features are based on Kakarala`s generalization of the bispectrum to compact Lie groups and a projection onto the sphere. I validated the method on a handwritten digit recognition dataset with randomly translated and rotated digits. Paper: http://arxiv.org/abs/cs.CV/0701127

This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2017 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity