Data Driven Image Models through Continuous Joint Alignment

This paper presents a family of techniques that we call congealing for modeling image classes from data. The idea is to start with a set of images and make them appear as similar as possible by removing variability along the known axes of variation. This technique can be used to eliminate "nuisance" variables such as affine deformations from handwritten digits or unwanted bias fields from magnetic resonance images. In addition to separating and modeling the latent images--i.e., the images without the nuisance variables--we can model the nuisance variables themselves, leading to factorized generative image models. When nuisance variable distributions are shared between classes, one can share the knowledge learned in one task with another task, leading to efficient learning. We demonstrate this process by building a handwritten digit classifier from just a single example of each class. In addition to applications in handwritten character recognition, we describe in detail the a...

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Erik G. Learned-Miller
• Keywords: Handwritten Digit, Images, Magnetic Resonance Images, PAMI 2006