Data Analytics and Methods for Improved Feature Selection and Matching
This work focuses on analysing and improving feature detection and matching. After creating an initial framework of study four main areas of work have been researched. These areas make up the main chapters within this thesis and focus on using the Scale Invariant Feature Transform (SIFT).
The mathematical preliminaries of SIFT investigates how this algorithm functions. Included is an analysis of the SIFT feature descriptor space and an investigation into the noise properties of SIFT. It introduces a novel use of the a contraio methodology and shows the success of this method as a way of discriminating between images which are likely to contain corresponding regions from images which do not.
Parameter analysis of SIFT uses both parameters sweeps and genetic algorithms as an intelligent means of setting the SIFT parameters for different image types utilising a GPU implementation of SIFT. The results have demonstrated which parameters are more important when optimising the algorithm and the areas within the parameter space to focus when tuning the values.
A multi-exposure, High Dynamic Range, fusion features process has been developed where SIFT image features are matched within high contrast scenes. Bracketed exposure images are analysed and features are extracted and combined from the different images to create a set of features which describe a larger dynamic range. They are shown to reduce the effects of noise and artefacts that are introduced when extracting feature from HDR images directly and have a superior image matching performance.
The final area is the development of a novel, 3D-based, SIFT weighting technique which utilises the 3D data from a pair of stereo images to cluster and class matched SIFT features. Weightings are applied to the matches based on the 3D properties of the features and how they cluster in order to attempt to discriminate between correct and incorrect matches using the a contrario methodology. The results show that the technique provides a method for discriminating between correct and incorrect matches and that the a contrario methodology has potential for future investigation as a method for correct feature match prediction.
This work, entitled Data Analytics and Methods for Improved Feature Selection and Matching, was submitted in April 2012 by Michael May to The University of Manchester for the degree of Doctor of Philosophy