A. J. Lacey

An Evaluation of the Performance of RANSAC Algorithms for Stereo Camera Calibration

This paper compares the use of RANSAC for the determination of epipolar geometry for calibrated stereo reconstruction of 3D data with more conventional optimisation schemes. The paper illustrates the poor convergence efficiency of RANSAC which is explained by a theoretical relationship describing its dependency upon the number of model parameters. The need for an a-priori estimate of outlier contamination proportion is also highlighted. A new algorithm is suggested which attempts to make better use of the solutions found during the RANSAC search while giving a convergence criteria which is independent of outlier proportion. Although no significant benefit can be found for the use of RANSAC on the problem of stereo camera calibration estimation. The new algorithm suggests a simple way of improving the efficiency of RANSAC searches which we believe would be of value in a wide range of machine vision problems.

Bayesian and non-Bayesian probabilistic models for medical image analysis

Abstract Bayesian approaches to data analysis are popular in machine vision, and yet the main advantage of Bayes theory, the ability to incorporate prior knowledge in the form of the prior probabilities, may lead to problems in some quantitative tasks. In this paper we demonstrate examples of Bayesian and non-Bayesian techniques from the area of magnetic resonance image (MRI) analysis. Issues raised by these examples are used to illustrate difficulties in Bayesian methods and to motivate an approach based on frequentist methods. We believe this approach to be more suited to quantitative data analysis, and provide a general theory for the use of these methods in learning (Bayes risk) systems and for data fusion. Proofs are given for the more novel aspects of the theory. We conclude with a discussion of the strengths and weaknesses, and the fundamental suitability, of Bayesian and non-Bayesian approaches for MRI analysis in particular, and for machine vision systems in general.

Validating MRI Field Homogeneity Correction Using Image Information Measures

For image analysis techniques to be of utility in medical diagnosis systems it is necessary to be able to perform quality control over the results they produce. Input data must conform to the assumptions within the algorithm if useful results are to be achieved. Automation of this process is essential if vision algorithms are to form components in analysis systems. In this paper we present a technique to validate the correction of field inhomogeneity in MR images. The initial intention was to use information measures to check the improvement due to correction. However, it will be shown that the standard log entropy calculation for information measurement does not have the required properties, specifically grey-level scale invariance. We present an alternative, scale-invariant information measure derived using conventional likelihood approaches, that can be applied as an absolute measure of information content. We show this technique in use for the validation of our existing coil correction method.

TINA 2001: The Closed Loop 3D Model Matcher

This paper discusses advances made to the 3D geometrical model matching system within the TINA machine vision environment over the last 10 years including the recent inclusion of a verification stage. We show how this final step closes the loop on the object location system allowing the theoretical location performance to be attained, eliminating key assumptions used in the existing forward pass algorithms. We explain how the use of a system approach has been crucial to the development of key components and summarise our findings.

Locating Motion Artifacts in Parametric fMRI Analysis

In this paper we assess rigid body co-registration in terms of residual motion artifacts for the different correlation approaches used in fMRI. We summarise, from a statistical perspective, the three main approaches to parametric fMRI analysis and then present a new way of visualising motion effects in correlation analysis. This technique can be used both to select regions of relatively unambiguous activation and to verify the results of analysis. We demonstrate the usefulness of this visualisation technique on fMRI data sets suffering from motion correlated artifacts. We use it in our assesment of rigid body co-registration concluding that it is an acceptable basis for re-alignment, provided that correlation is done using a measure which estimates variance from the data at each voxel.