The main area of research delineated in this thesis provides instances when Computer vision based technology has shown tremendous productivity gains in the Oil sands industry in Fort McMurray, Alberta, Canada. Specifically, the interface between Bitumen-froth (crude oil) and the Middlings (Sand) in separation cells (during the extraction process) is estimated in real time from camera video and used for automatic control of the interface level. Two original algorithms have been developed which solve the interface estimation problem using techniques ranging from image analysis, estimation theory (Particle filters) and probabilistic reasoning. These ideas are discussed in chapters three and four. The first chapter of this thesis discusses the broad area of Computer vision research as a knowledge basis for the current work. Computer vision (automatic image analysis) has been presented starting from the basics and culminating in advanced algorithms that are used frequently. The methods described in this chapter form the foundation of the work that follows in the subsequent chapters. After the introduction to automatic image analysis, a set of Monte Carlo simulation based methods called Particle filters are introduced in the second chapter. These Monte Carlo filters assume importance in the current work as they are used to derive one of the main results of this thesis. A large part of this chapter though is devoted to the introduction of the concept of measure theoretic probability which is used in proving the convergence of Particle filters. Another application of Computer vision techniques is also developed in this thesis (in chapter five) to treat the problem of automatic interface and boundary detection in X-ray view cell images. These images are typically used to observe liquid-liquid and liquid-vapour phase behaviour of heavy oils such as Bitumen in chemical equilibrium investigations. The equilibrium data would then be used to enhance Bitumen separation technologies. Manual tracking of the interfaces between these phases for different mixtures and conditions is time consuming when a large set of such images are to be analysed. A novel algorithm is developed that is based on state-of-the-art in Computer vision techniques and automates the entire task. / Process Control

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