Progress Report

March 15th, 2005

Protein Gel Registration Experiments

• Images are similar (in their general form)
• They comprise many small separate elements
• One-to-one correspondence exists
• Discrepancy image is rough after a short registration
• shown in the next slide

Protein Gel Registration Experiments - Ctd.

• CPS warps are used to achieve this
• Further efforts have been conceded at this stage

Model Evaluation Improvements

• GUI tools for syntheses generation
• Generation was made about 5 times quicker
• Gaussian distribution for choice of syntheses
• An option for a flat distribution still exists
• Many more syntheses attempted recently
• 10,000 at most, IPMI experiment included 1,000
• This results in heavy calculations
• 1,040,000 'distances' for the brain experiments

VXL QT Tools

• Synthesis from a model added to the GUI
• More options (customisations) are available

VXL QT Tools - Ctd.

• There are more possible extensions like automated evaluation
• Implementation of these would be effort-consuming

Model Evaluation Experiments

• Further model evaluations
• Similar experiments to ones in the IPMI submission
• Gaussian distribution to draw syntheses
• Much larger experiments (~7 days run-time for each)
• Run in parallel (lowered significantly in scheduler)

Model Evaluation Experiments - Explanation

• The following are a few brief notes
• Recapitulate the problem being solved
• Explain how the concepts were extended

Model Evaluation Experiments - Explanation - Ctd.

• A collection of raw brains given
• Data from Paul Bromiley: 104 brain slices, affinely-aligned

Model Evaluation Experiments - Explanation - Ctd.

• Syntheses are produced from model
• These tend to be smaller in size
• A little fuzzy if instantiated after automatic model-building

• 36 hand-annotated brains available too

Model Evaluation Experiments - Framework

Model Evaluation Experiments - Distances

• All possible training image-synthesis pairings are considered
• Distance is calculated using the shuffle transform
• A matrix of distances is obtained

Model Evaluation Experiments - Measures

• Specificity and generalisability are derived directly from the matrix
• Various shuffle radii/window sizes attempted

Model Evaluation Experiments

• C0: Pairwise, 16 x 16 grid, residuals evaluated in reference frame
• C1: Pairwise, 16 x 16 grid, residuals evaluated in training image frame
• C2: Pairwise, 16 x 16 grid, ignoring low variance regions

Model Evaluation Experiments - Ctd.

• C3: Pairwise, 16 x 16 grid, ignoring low variance regions, moving to strong edges
• C4: Groupwise, (points as C3), (mean only) - i.e. 'Initialisation' algorithm
• C5: Groupwise, (points as C3), including shape model [width=10]

Model Evaluation Experiments - Results

• 10,000 synthetic images
• 10 most principal modes of variation
• All drawn from a normal distribution

Model Evaluation Experiments - Specificity

• Some interesting behaviour to analyse

Model Evaluation Experiments - Generalisability

• Relatively noisy, no valid explanation yet

Face Experiments

• Set of face images obtained from Tim
• 68 faces from Germany; quite diverse
• Many glasses and beards; pose and light variation
• Images are marked up, but not aligned
• Images were later rigidly aligned
• Face model evaluation in progress

Faces - Examples

• Below are two images from the set after rigid alignment under MATLAB

• The black frames near the borders are due to alignment

Models Sigma

• Models can then be built from the set

Models Sigma - Ctd.

• Perturbation of the landmarks affects model quality

Perturbation with Sigma=3

Evaluation Difficulties

• Note the background artefacts in original images
• These interfere with sensible distance (similarity) metrics

Masked Faces

• The background is omitted from all images

• This obtains a new model
• The training set becomes more easily-comparable

Masked Faces - Synthesis

• Some of the synthetic images from the model:

• Similarity to images from the training set becomes clearer
• Images still need to be re-scaled to overlap

Face Model Evaluation

• Current experiments investigate landmark noise
• Sigma ranging from 0 to 6 inclusive
• Distance measures based on 1000 syntheses
• Carole yet to give generalisability and specificity figures
• Specificity is expected (or hoped) to rise as a function of sigma

Miscellany

• MATLAB Meeting
• MICCAI, BMVC, MIUA Deadlines
• Recommendation: Google Desktop