No description available.

No description available.

No description available.

No description available.

This thesis describes the research carried out in overcoming the problems encountered during the development of an autonomous mobile robot (AMR) which uses a single television camera for navigation in environments with visible edges, such as corridors and hallways. The objective was to determine the minimal sensing and signal processing requirements for a real AMR that could achieve self-steering, navigation and obstacle avoidance in real unmodified environments. A goal was to design algorithms that could meet the objective while being able to run on a laptop personal computer (PC). This constraint confined the research to computationally efficient algorithms and memory management techniques. The methods by which the objective was successfully achieved are described. A number of noise reduction and feature extraction algorithms have been tested to determine their suitability in this type of environment, and where possible these have been modified to improve their performance. The current methods of locating lines of perspective and vanishing points in images are described, and a novel algorithm has been devised for this application which is more efficient in both its memory usage and execution time. A novel obstacle avoidance mechanism is described which is shown to provide the low level piloting capability necessary to deal with unexpected situations. The difficulties of using a single camera are described, and it is shown that a second camera is required in order to provide robust performance. A prototype AMR was built and used to demonstrate reliable navigation and obstacle avoidance in real time in real corridors. Test results indicate that the prototype could be developed into a competitively priced commercial service robot.

No description available.

A novel approach for the analysis of spinal cord deformation based on a combined technique of non-invasive imaging and medical image processing is presented. A sopposed to traditional approaches where animal spinal cords are exposed and directly subjected to mechanical impact in order to be examined, this approach can be used to quantify deformities of the spinal cord in vivo, so that deformations — specifically those of myelopathy-related sustained compression — of the spinal cord can be computed in its original physiological environment. This, then, allows for a more accurate understanding of spinal cord deformations and injuries. Images of rat spinal cord deformations, acquired using magnetic resonance imaging (MRI), were analyzed using a combination of various image processing methods, including image segmentation, a versor-based rigid registration technique, and a B-spline-based non-rigid registration technique. To verify the validity and assess the accuracy of this approach, several validation schemes were implemented to compare the deformation fields computed by the proposed algorithm against known deformation fields. First, validation was performed on a synthetically-generated spinal cord model data warped using synthetic deformations; error levels achieved were consistently below 6% with respect to cord width, even for large degrees of deformation up to half of the dorsal-ventral width of the cord (50% deflection). Then, accuracy was established using in vivo rat spinal cord images warped using those same synthetic deformations; error levels achieved were also consistently below 6% with respect to cord width, in this case for large degrees of deformation up to the entire dorsal-ventral width of the cord (100% deflection). Finally, the accuracy was assessed using data from the Visible Human Project (VHP) warped using simulated deformations obtained from finite element (FE) analysis of the spinal cord; error levels achieved were as low as 3.9% with respect to cord width. This in vivo, non-invasive semi-automated analysis tool provides a new framework through which the causes, mechanisms, and tolerance parameters of myelopathy-related sustained spinal cord compression, as well as the measures used in neuroprotection and regeneration of spinal cord tissue, can be prospectively derived in a manner that ensures the bio-fidelity of the cord.

This research focuses on the development of a novel image super-resolution algorithm for coordinate measurement in manufacturing. The main features of the algorithm are that it is fast, flexible and fully automatic. A fast algorithm is required because image-super resolution is a procedure that handles a large amount of data. Having a slow or highly complex algorithm may result in computational infeasibility. A flexible algorithm means the algorithm can be customised to handle specific problems, i.e. the algorithm can be augmented with multiple constraints and still obtain an optimal solution. This is desirable as most image super-resolution problems are specific and having the capacity to augment multiple constraints reduces the search space, thus leading to faster convergence. An automatic algorithm is viewed as ideal as it has minimum human intervention and will generate super-resolution images automatically when measured frames are input. / This study considers three issues related the developing the algorithms: the model of image super-resolution; the formulation of a flexible algorithm that is capable of augmenting multiple constraints into the model and produces optimal super-resolution images; and the optimisation technique to solve the problem formulated to ensure that the computational complexity is low. / Thesis (PhDEngineering)--University of South Australia, 2005.

No description available.

Bibliography: leaves 100-110. / xiv, 110 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis addresses the structure from motion problem in computer vision. / Thesis (Ph.D.)--University of Adelaide, Dept. of Computer Science, 2000