Camera Self-Calibration: Theory and Experiments
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Lens distortion calibration using point correspondences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Rational Function Lens Distortion Model for General Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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As the study of complex systems has become dominant in physics the link between computational and physical science has become ever more important. In particular, with the rising popularity of imaging techniques in physis, the development and application of cutting edge computer vision techniques has become vital. Here we present novel image analysis methods which can be used to extract the position of features in diffraction patterns (reciprocal space) with unprecedented accuracy. The first contribution we have developed is a method for calculating the nonlinear response of photographic film by using the noise in the image enabling the extraction of accurate intensity information. This allows high-resolution (but non-linear) film to be used in place of low-resolution (but linear) CCD cameras. The second contribution is a method for accurately localising very faint features in diffraction patterns by modelling the features and using the expectation maximization algorithm directly on the image to fit them. The accuracy of this technique has been verified by testing it on synthetic data. These methods have been applied to transmission electron microscopy data, and have already enabled discoveries which would have been impossible using previously available techniques.