[BIC-announce] FW: PhD Defense for Rupert Brooks, August 25/08- abstract

[Jennifer Chew, Ms.]

FOR YOUR INFORMATION.  JENNIFER  

CIM PhD candidates, Rupert Brooks, supervised by Prof. Tal Arbel, will
defend his thesis on:

Monday
August 25, 2008
2:00 pm
MC603 McConnell Engineering Building

The abstract is below.



Efficient and Reliable Methods for
Direct Parameterized Image Registration

Abstract
	
This thesis examines methods for efficient and reliable image
registration in the context of computer vision and medical imaging.
Direct, parameterized image registration approaches work by minimizing a
difference measure between a fixed reference image, and the image warped
to match it. The calculation of this difference measure is the most
computationally intensive part of the process and for faster
registration it either has to be calculated faster, or calculated
fewer times.   Both possibilities are addressed in detail.

Efficiency and reliability are addressed in four ways (1) Methods are
presented for generalizing the Gauss-Newton Hessian approximation to the
non-least squares case, and for the optimal selection of scaling factors
for the transformation parameters.  Both of these enhance performance by
enabling optimization algorithms to perform fewer evaluations of the
difference measure.  The performance of a wide range of optimization
algorithms is analyzed both theoretically and experimentally, and
guidelines are presented for optimizer selection based on the
characteristics of the registration problem. (2) Using only a portion of
the available pixels results in faster calculation but suffers from a
potential loss of accuracy.  An algorithm is presented which applies
formal deliberation control methods to managing this tradeoff.  By
managing the amount of image data used at every evaluation of the cost
function, the algorithm adapts to the nature of the images and the stage
of the optimization. This adaptive approach allows greater efficiency
without sacrificing reliability.
(3) It is shown that the scale used to compute the derivative is a
critical factor to consider when selecting subsets of pixels for
registration, that has largely been ignored in previous work.
Finally, (4) two existing efficient registration approaches, the inverse
compositional, and efficient second order algorithms, rely on
specialized optimizer update steps and specialized parameterizations.
  A generalization of these methods is presented that both identifies
the connections between them, and eliminates the need for these
specialized components.

Throughout the thesis, application specific approaches have been
avoided.  Both 2D and 3D images from both computer vision and medical
imaging applications have been used throughout.  Consequently each of
the efficient registration methods can be applied, alone or in
combination, to a very wide range of problems.


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