Benedek, Csaba (2008) Novel Markovian Change Detection Models in Computer Vision. PhD thesis, -.

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Abstract

In this thesis novel probabilistic models are proposed for three different change detection tasks of computer vision, primarily focusing on applications from video surveillance and aerial exploitation. The surveys are performed in a coherent Markov Random Field (MRF) segmentation framework, but the introduced models face different practical challenges such as shadow effects, image registration errors or presence of noisy and incomplete concept descriptors. Contributions are presented in efficient feature extraction, probabilistic modeling of natural processes and feature integration via local innovations in the model structures. We show by several experiments that the proposed novelties embedded into a strict mathematical toolkit can significantly improve the results in real world test images and videos.

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