Use this URL to cite or link to this record in EThOS:
Title: Motion segmentation of semantic objects in video sequences
Author: Thirde, David J.
ISNI:       0000 0001 3516 9197
Awarding Body: Kingston University
Current Institution: Kingston University
Date of Award: 2007
Availability of Full Text:
Access from EThOS:
Access from Institution:
The extraction of meaningful objects from video sequences is becoming increasingly important in many multimedia applications such as video compression or video post-production. The goal of this thesis is to review, evaluate and build upon the wealth of recent work on the problem of video object segmentation in the context of probabilistic techniques for generic video object segmentation. Methods are suggested that solve this problem using formal probabilistic learning techniques, this allows principled justification of methods applied to the problem of segmenting video objects. By applying a simple, but effective, evaluation methodology the impact of all aspects of the video object segmentation process are quantitatively analysed. This research focuses on the application of feature spaces and probabilistic models for video object segmentation are investigated. Subsequently, an efficient region-based approach to object segmentation is described along with an evaluation of mechanisms for updating such a representation. Finally, a hierarchical Bayesian framework is proposed to allow efficient implementation and comparison of combined region-level and object-level representational schemes.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Computer science and informatics