CSSE Seminar Series (CSSESS)

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Adrian presents his newly defended Ph. D thesis. In the domain of computer vision, registration is the process of calculating the transformation between a known object, called a marker, and a camera which is viewing it. Registration is the foundation for a number of applications across a range of disciplines, such as augmented reality, medical imaging and robotic navigation. The transformation between the camera and the marker is generally described as having six degrees of freedom, as it is comprised of three independent translations and three rotations. In the set of two dimensional planar markers, there are two classes of marker: (1) fiducial, which are designed to be easily recognisable by computers but have little to no semantic meaning to people, and (2) natural feature, which have meaning to people, but can still be registered by a computer. As computers become more powerful, natural feature markers are increasingly the more popular choice, however there are still a number of inherent problems with this class of marker. This thesis examines the most common shortcomings of natural feature markers, and proposes and evaluates solutions to these weaknesses. The work starts with a review of the existing planar registration approaches, both fiducial and natural feature, with a focus on the strengths and weaknesses of each. From this review, the theory behind planar registration is discussed, from the different co-ordinate systems and transformations, to the computation of the registration transformation. With a foundation of planar registration, natural feature registration is decomposed into its core steps, and each stage is described in detail. This leads into a discussion of the complete natural feature registration pipeline, highlighting common issues encountered at each step, and discussing the possible solutions for each issue. A new implementation of natural feature registration called the Opticalflow Perspective Invariant Registration Augmentation (OPIRA) is proposed, which provides vast improvements in robustness to perspective, rotation and changes in scale to popular registration algorithms such as SIFT and SURF. OPIRA is shown to improve perspective invariance by 22% and 16% for SIFT and SURF respectively, as well as increase rotation invariance from 22 to the full 360 in the rotation dependent implementations of these algorithms. From the investigation into problems and potential resolutions at each stage during registration, each proposed solution is evaluated empirically against an external ground truth. The results are discussed and a conclusion on the improvements gained by each proposed solution and the feasibility of use in a real natural feature registration application is drawn. Finally, some applications which use the research contained within this thesis are described, as well as some future directions for the research.

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