The turbulent atmosphere degrades images of objects viewed through it by introducing random amplitude and phase errors into the optical wavefront. Various methods have been devised to obtain true images of such objects, including the shift-and-add method, which is examined in detail in this work. It is shown theoretically that shift-and-add processing may preserve diffraction-limited information in the resulting image, both in the point source and extended object cases, and the probability of ghost peaks in the case of an object consisting of two point sources is discussed. Also, a convergence rate for the shift-and-add algorithm is established and simulation results are presented. The combination of shift-and-add processing and Wiener filtering is shown to provide excellent image restorations.

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The behavior of digital cross-correlation algorithms as applied to image matching problems is examined in terms of the relationship between measureable image properties and algorithm characteristics. Statistical image quality measures are developed which could be employed in a preprocessor to predict the performance of automatic stereo-compilation equipment. The measures include a quantity derived from the Cramer-Rao lower bound on the variance of any unbiased parameter estimator, various contrast measures such as variance, contrast modulation, and median absolute deviation, and a stationarity detector related to the variance gradient. These measures are based on image and correlator models which describe the behavior of correlation processors under conditions of low image contrast or signal-to-noise ratio, geometric distortion, and image non-stationarity. Computer simulations using synthetic imagery were performed to verify the various models, and indicate the potential for the use of image quality measures in the predicting of correlation behavior. Implications of the models in terms of correlation processor design and implementation are discussed.

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A major problem in robotics vision is the segmentation of images of natural scenes in order to understand their content. This thesis presents a new solution to the image segmentation problem that is based on the design of a rule-based expert system. General knowledge about low level properties of an image is formulated into production rules. A number of processes employ the rules to segment the image into uniform regions and connected lines. In addition to the knowledge rules, a set of control rules are also employed. These include meta-rules that embody inferences about the order in which the knowledge rules are matched. They also include focus of attention rules that determine the path of processing within the image. A third set of rules contains the strategy rules which are data-driven inferences about the control rules. They dynamically modify the processing strategy. Different rule ordering and focus of attention strategies are selected according to a set of performance parameters. These measure the quality of the segmentation output at any point in time. Experiments with the knowledge rules resulted in an optimal set based on output quality and processing efficiency. Overall system performance is shown to be qualitatively and quantitatively superior to previous segmentation algorithms.

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No description available.

Although motion processing in insects has been extensively studied for over almost 40 years, velocity detection in insects and how the insect brain computes the velocity of a moving feature, independent of its size or contrast, is a major enigma that remains unsolved. This study examines the accuracy of velocity estimation using two biologi-cally inspired models of motion detection, (i) the Horridge template model and (ii) the Reichardt correlator model. Various extensions and enhancements of these models are implemented with the goal of achieving robust velocity measurements. The template model is one of the prominant models of motion detection, which was proposed by Horridge in 1990. This Thesis further extends this model with the aim of improving accuracy in velocity detection using chrominance as well as luminance channels with various error checking mechanisms using different stimuli. Then the template model response is compared with Dror's elaborated Reichardt model and electro-physiological experimental results obtained from the fly visual system using similar stimuli in each case. A modified Reichardt model is shown to give a more similar response to that of fly neurons. In order to improve velocity performance of the Reichardt model, it is necessary to reduce contrast dependance of the correlator response as well as to make it independent of the structure of the visual stimuli. With this aim, the Reichardt model is then further elaborated to include contrast adaptation by a feedback adaptive mechanism and a clear reduction in contrast dependency is demonstrated. The deviation of the correlator response depending on the stimulus is termed as pattern noise. To reduce this pattern noise, the Reichardt correlator model is further extended to implement compressive non-linearity or saturation. It is seen that saturation has a profound effect on the shape of the pattern noise. Further studies on pattern noise is then performed in this Thesis using different stimuli at different speeds and contrasts. Work carried out in this Thesis on the affect of various receptive field shapes on the pattern noise reveals circular sampled arrays reduce pattern noise and hence, based on this result, a small 16 pixel yaw sensor using our elaborated model is built that shows promising performance with various potential applications. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1337180 / Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2008

Thesis (Ph.D.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (p. 180-185).