Automatic Construction of Parts+Geometry Models for Initialising Groupwise Non-rigid Registration

UoM administered thesis: Phd

  • Authors:
  • Pei Zhang

Abstract

Groupwise non-rigid image registration is a powerful tool to automatically establish correspondences across sets of images. Such correspondences are widely used for constructing statistical models of shape and appearance. As existing techniques usually treat registration as an optimisation problem, a good initialisation is required. Although the standard initialisation---affine transformation---generally works well, it is often inadequate when registering images of complex structures. In this thesis we present a sophisticated system that uses the sparse matches of one or more parts+geometry models as the initialisation. We show that both the model/s and its/their matches can be automatically obtained, and that the matches are able to effectively initialise a groupwise non-rigid registration algorithm, leading to accurate dense correspondences. We also show that the dense mesh models constructed during the groupwise registration process can be used to accurately annotate new images. We demonstrate the efficacy of the proposed system on three datasets of increasing difficulty, and report on a detailed quantitative evaluation of its performance.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award date1 Aug 2012