Thesis (M.Sc.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on May 7, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, [Department of] Electrical and Computer Engineering, University of Alberta. Includes bibliographical references.

Thesis (Ph.D.)--McMaster University, 2003. / Advisor: David W. Capson. Includes bibliographical references (leaves 199-207). Also available via World Wide Web.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2007. / Vachtsevanos, George, Committee Chair ; Heck, Bonnie, Committee Member ; Vela, Patricio, Committee Member ; Yezi, Anthony, Committee Member ; Johnson, Eric, Committee Member.

Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves 97-101).

Thesis (M.Sc.)--York University, 2005. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 112-117). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss &rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR11930

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references. Also available in electronic version.

Object recognition has been one of the main areas of research into computer vision in the last 20-30 years. Until recently most of this research has been performed on scenes taken using static monocular, binocular or even trinocular cameras. It is believed, however, that by adding the ability to move the look point and concentrate on a region of interest a more robust and efficient method of vision can be achieved. Recent studies into the ability to provide human-like vision systems for a more active approach to vision have lead to the development of a number of robot controlled vision systems. In this thesis the development of one such system at the University of Surrey, the stereo robot head "Getafix" is described. The design, construction and development of the head and its control system have been undertaken as part of this project with the aim of improving current vision tasks, in particular, that of object recognition. In this thesis the design of the control systems, kinematics and control software of the stereo robot head will be discussed. A number of simple commissioning experiments are also shown, using the concepts of the robot control developed herein. Camera lens control and calibration is also described. A review of classical primitive based object recognition systems is given and the development of a novel generic cylindrical object recognition strategy is shown. The use of this knowledge source is demonstrated with other vision processes of colour and stereo. The work on the cylinder recognition strategy and the stereo robot head are finally combined within an active vision framework. A purposive active vision strategy is used to detect cylindrical structures, that would otherwise be undetectable by the cylindrical object detection algorithm alone.

The extraction of geometric primitives from images is a fundamental task in computer vision. The objective of shape extraction is to find the position and recognise descriptive features of objects (such as size and rotation) for scene analysis and interpretation. The Hough transform is an established technique for extracting geometric shapes based on the duality definition of the points on a curve and their parameters. This technique has been developed for extracting simple geometric shapes such as lines, circles and ellipses as well as arbitrary shapes represented in a non-analytically tabular form. The main drawback of the Hough transform technique is the computational requirement which has an exponential growth of memory space and processing time as the number of parameters used to represent a primitive increases. For this reason most of the research on the Hough transform has focused on reducing the computational burden for extracting simple geometric shapes. This thesis presents two novel techniques based on the Hough transform approach, one for ellipse extraction and the other for arbitrary shape extraction. The ellipse extraction technique confronts the primary problems of the Hough transform, namely the storage and computational load, by considering the angular changes in the position vector function of the points in an ellipse. These changes are expressed in terms of sets of points and gradient direction to obtain simplified mappings which split the five-dimensional parameter space required for ellipse extraction into two two-dimensional and one one-dimensional spaces. The new technique for arbitrary shape extraction uses an analytic representation of arbitrary shapes. This representation extends the applicability of the Hough transform from lines and quadratic forms, such as circles and ellipses, to arbitrary shapes avoiding the discretisation problems inherent in current (tabular) approaches. The analytic representation of shapes is based on the Fourier expansion of a curve and the extraction process is formulated by including this representation in a general novel definition of the Hough transform. In the development of this technique some strategies of parameter reduction are implemented and evaluated.

Texture plays an increasingly important role in computer vision. It has found wide application in remote sensing, medical diagnosis, quality control, food inspection and so forth. This thesis investigates the problem of classifying texture in digital images, following the convention of splitting the problem into feature extraction and classification. Texture feature descriptions considered in this thesis include Liu's features, features from the Fourier transform using geometrical regions, the Statistical Gray-Level Dependency Matrix, and the Statistical Feature Matrix. Classification techniques that are considered in this thesis include the K-Nearest Neighbour Rule and the Error Back-Propagation method. Novel techniques developed during the author's Ph.D study include (1) a Generating Shrinking Algorithm that builds a three-layer feed-forward network to classify arbitrary patterns with guaranteed convergence and known generalisation behaviour, (2) a set of Statistical Geometrical Features for texture analysis based on the statistics of the geometrical properties of connected regions in a sequence of binary images obtained from a texture image, (3) a neural implementation of the K-Nearest Neighbour Rule that can complete a classification task within 2K clock cycles. Experimental evaluation using the entire Brodatz texture database shows that (1) the Statistical Geometrical Features give the best performance for all the considered classifiers, (2) the Generating Shrinking Algorithm offers better performance over the Error Back-Propagation method and the K-Nearest Neighbour Rule's performance is comparable to that of the Generating Shrinking Algorithm, (3) the combination of the Statistical Geometrical Features with the Generating-Shrinking Algorithm constitutes one of the best texture classification systems considered.

Intelligent Co-Pilot Systems (ICPS) offer the next challenge to vehicle-highway automation. The key to ICPSs is the detection of moving objects (other vehicles) from the moving observer using a visual sensor. The aim of the work presented in this thesis was to design and implement a feature detection and tracking strategy that is capable of tracking image features independently, in parallel, and in real-time and to cluster/segment features utilising the inherent temporal information contained within feature trajectories. Most images contain areas that are of little or no interest to vision tasks. An attentive, data-driven, approach to feature detection and tracking is proposed which aims to increase the efficiency of feature detection and tracking by focusing attention onto relevant regions of the image likely to contain scene structure. This attentive algorithm lends itself naturally to parallelisation and results from a parallel implementation are presented. A scene may be segmented into independently moving objects based on the assumption that features belonging to the same object will move in an identical way in three dimensions (this assumes objects are rigid). A model for scene segmentation is proposed that uses information contained within feature trajectories to cluster, or group, features into independently moving objects. This information includes: image-plane position, time-to-collision of a feature with the image-plane, and the type of motion observed. The Multiple Model Adaptive Estimator (MMAE) algorithm is extended to cope with constituent filters with different states (MMAE2) in an attempt to accurately estimate the time-to-collision of a feature and provide a reliable idea of the type of motion observed (in the form of a model belief measure). Finally, poor state initialisation is identified as a likely prime cause for poor Extended Kalman Filter (EKF) performance (and hence poor MMAE2 performance) when using high order models. The idea of the neurofuzzy initialised EKF (NF-EKF) is introduced which attempts to reduce the time for an EKF to converge by improving the accuracy of the EKF's initial state estimates.