Use this URL to cite or link to this record in EThOS:
Title: Robust statistical deformable models
Author: Antonakos, Epameinondas
ISNI:       0000 0004 6496 9056
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
During the last few years, we have witnessed tremendous advances in the field of 2D Deformable Models for the problem of landmark localization. These advances, which are mainly reported on the task of face alignment, have created two major and opposing families of methodologies. On the one hand, there are the generative Deformable Models that utilize a Newton-type optimization. This family of techniques has attracted extensive research effort during the last two decades, but has lately been criticized of achieving inaccurate performance. On the other hand, there is the currently predominant family of discriminative Deformable Models that treat the problem of landmark localization as a regression problem. These techniques commonly employ cascaded linear regression and have proved to be very accurate. In this thesis, we argue that even though generative Deformable Models are less accurate than discriminative, they are still very valuable for several tasks. In the first part of the thesis, we propose two novel generative Deformable Models. In the second part of the thesis, we show that the combination of generative and discriminative Deformable Models achieves state-of-the-art results on the tasks of (i) landmark localization and (ii) semi-supervised annotation of large visual data.
Supervisor: Zafeiriou, Stefanos ; Pantic, Maja Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral