BibTeX entry

@PHDTHESIS{201002S_N_Coupe,
  AUTHOR={S N Coupe},
  TITLE={Machine Learning of Projected 3D Shape},
  SCHOOL={University of Manchester},
  MONTH=Feb,
  YEAR=2010,
  URL={http://www.bmva.org/theses/2010/2010-coupe.pdf},
}

Abstract

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 research 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, exploring 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 methodologies, a quantitative scheme for accurately localising and verifying the presence of hypothesised image-projected 3D edge-feature models has been implemented. Full view-sphere sampled 3D model matching tests have been conducted for the competing methodologies, identifying significant shortfalls with the stereo-based approach to 3D model matching. The more powerful and reliable view-based techniques are subsequently analysed with regard to the more demanding task of comparative 3D object recognition.