Machine Learning of Projected 3D Shape

This thesis primarily investigates the potential of the Pairwise Geometric Histogram (PGH) representation as the basis of a machine learning edge and view-based 3D object recognition computer vision system. The work extends 20 years ’ worth of associated research within the TINA computer vision research group [1]. PGHs have formerly been engineered as a solution to the presented problem, directly addressing all of the invariance characteristics required by such a representation. Previous re-search has proven the power of the proposed techniques for 2D object recognition through difficult, real-world viewing conditions including scene clutter and occlusion. This project extends the associated methodologies into the third dimension, explo-ring methods for representing scaled 3D objects ’ continuous appearances around their view-spheres. The research agenda has also included a comparative analysis of the pre-existing TINA [1] stereo vision-based 3D Model Matching (3DMM) system, which is able to localise specified 3D objects in 3D scenes. In support of both mono and stereo metho