Progress Report

April 19th, 2005

Overview

The Idea

Measure overlap in hyperspace

The Idea

Compute Specificity and Generalisation ability

The Idea

Repeat for all cross-pairings

Measuring Distance

Use the shuffle transform

Measuring Distance

Shuffle: face example

Measuring Distance

Shuffle: face example

Shuffle: brain example

Measuring Distance

Shuffle: brain example

Measuring Distance

Distance matrix for the sets

Models - Face

Input images

Models - Face

Poor output image

Models - Face

Models changed by perturbation of landmark points

Models - Face

Models changed by large deformation of training images

Models - Face

Models changed by smaller deformation of training images

Models - Face

The correct model (Surrey database)

Models - Brain

Model automatically built by group-wise registration

Models - Brain

Warped models (CPS)

Radically-warped models (GIMP)

Models - Brain

Sequence of warps and difference images (bottom)

Evaluation of models - Faces

Generalisation ability as landmark points are shifted

Evaluation of models - Faces

Specificity as landmark points are shifted

Evaluation of models - Brains

Generalisation ability as landmark points are shifted

Evaluation of models - Brains

Specificity as landmark points are shifted

Evaluation of models - Brains

Specificity as images in the training set are deformed

Comparison - Brain Model Construction

Generalisation ability for the different approaches

Comparison - Brain Model Construction

In more depth

Comparison - Brain Model Construction

Specificity for the different approaches

Comparison - Brain Model Construction

In more depth

Comparing Different Shuffle Distances

Generalisation ability for the different window sizes

Comparing Different Shuffle Distances

Specificity for the different window sizes

Normalisation - Framework

The method of normalising (perhaps not practically useful)

Normalisation

Generalisation ability for the original training set (brain)

Normalisation

Generalisation ability for the pseudo training set

Normalisation

Normalised Generalisation ability

Normalisation

Normalised Specificity