Abstract

The thesis describes novel methods for image motion computation and template matching. A multiscale algorithm for energy-based estimation and representation of local spatiotemporal structure by second order symmetric tensors is presented. An efficient spatiotemporal implementation of a signal modelling method called normalized convolution is described. This provides a means to handle signals with varying degree of reliability. As an application of the above results, a smooth pursuit motion tracking algorithm that uses observations of both target motion and position for camera head control and motion prediction is described. The target is detected using a novel motion field segmentation algorithm which assumes that the motion fields of the target and its immediate vicinity, at least occasionally, each can be modelled by a single parameterized motion model. A method to eliminate camera-induced background motion in the case of a pan/tilt rotating camera is suggested. In a second application, a high-precision image motion estimation algorithm performing clustering in motion parameter space is developed. The algorithm, which can handle

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