David W. Murray

Learn More
This paper has two goals. The first is to develop a variety of robust methods for the computation of the Fundamental Matrix, the calibration-free representation of camera motion. The methods are drawn from the principal categories of robust estimators, viz. case deletion diagnostics, M-estimators and random sampling, and the paper develops the theory(More)
We have developed a 12-item questionnaire for patients having a total knee replacement (TKR). We made a prospective study of 117 patients before operation and at follow-up six months later, asking them to complete the new questionnaire and the form SF36. Some also filled in the Stanford Health Assessment Questionnaire (HAQ). An orthopaedic surgeon completed(More)
We developed a 12-item questionnaire for completion by patients having total hip replacement (THR). A prospective study of 220 patients was undertaken before operation and at follow-up six months later. Each completed the new questionnaire as well as the SF36, and some the Arthritis Impact Measurement Scales (AIMS). An orthopaedic surgeon assessed the(More)
Camera phones are a promising platform for hand-held augmented reality. As their computational resources grow, they are becoming increasingly suitable for visual tracking tasks. At the same time, they still offer considerable challenges: Their cameras offer a narrow field-of-view not best suitable for robust tracking; images are often received at less than(More)
The orientation of an acetabulum or an acetabular prosthesis may be described by its inclination and anteversion. Orientation can be assessed anatomically, radiographically, and by direct observation at operation. The angles of inclination and anteversion determined by these three methods differ because they have different spatial arrangements. There are(More)
ÐAn active approach to sensing can provide the focused measurement capability over a wide field of view which allows correctly formulated Simultaneous Localization and Map-Building (SLAM) to be implemented with vision, permitting repeatable longterm localization using only naturally occurring, automatically-detected features. In this paper, we present the(More)
MLESAC is an established algorithm for maximum-likelihood estimation by random sampling consensus, devised for computing multiview entities like the fundamental matrix from correspondences between image features. A shortcoming of the method is that it assumes that little is known about the prior probabilities of the validities of the correspondences. This(More)