Thirde, David J. (2007) Motion segmentation of semantic objects in video sequences. (PhD thesis), Kingston University.Full text not available from this archive.
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.
|Item Type:||Thesis (PhD)|
|Physical Location:||This item is held in stock at Kingston University Library.|
|Research Area:||Computer science and informatics|
|Faculty, School or Research Centre:||Faculty of Computing, Information Systems and Mathematics (until 2011)|
|Depositing User:||Automatic Import Agent|
|Date Deposited:||09 Sep 2011 21:38|
|Last Modified:||02 Oct 2014 10:27|
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