| Summary: | This thesis investigates robust and fast methods for single and cooperative 2D/3D image mosaicing to enhance field of view of images by joining them together. Image mosaicing is underlined by the process of image registration and a significant portion of the contributions of this work are dedicated to it. Image features are identified as a solution to the problem of image registration that uses feature-to-feature matching between images to solve for inter-image transformations. We have developed a novel two signature distribution based feature descriptor that combines grey level gradients and a colour histogram. This descriptor is robust to illumination changes and shows better matching accuracy compared to state of the art. Furthermore, we introduce a feature clustering technique that uses colour codes assigned to each feature to group them together. This allows fast and accurate feature matching as the search space is reduced. Taking into account feature location uncertainty we have introduced a novel information fusion technique to reduce this error by covariance intersection. This reduced error location is consequently fed to an H∞ filter taking into account system uncertainty for parameter estimation. We show that this technique outperforms costly nonlinear optimisation techniques. We have also developed a novel coupled filtering scheme based on H∞ filtering that estimates inter-image geometric and photometric transformations simultaneously. This is shown to perform better than standard least square techniques. Furthermore, we have introduced time varying parameter estimation using recursive techniques that facilitate in tracking changing parameters of inter-image transformations, suitable for image mosaicing between moving platforms. A method for rapid 3D scene reconstruction is developed that uses homographic lines between images for semi-dense pixel matching. Triangular meshes are then used for a complete visualisation of the scene and to fill in the gaps. To tackle cooperative mosaicing scenarios, additional methods are presented that include descriptor compression using principal components and 3D scene merging using the trifocal tensor. Capabilities of the proposed techniques are illustrated with real world images.
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