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This dissertation presents a novel underwater machine vision technique which uses the optical properties of water to extract range information from colour images. By exploiting the fact that the attenuation of light in water is a function of frequency, an intensity-range transformation is developed and implemented to provide monocular vision systems with a three-dimensional scene reconstruction capability. The technique can also be used with images that have no salient, contrasting features and there are no restrictions on surface shapes. From a generalized reflectance map based on the optical properties of water, the closed form intensity-range transformation is derived to convert intensity images from various spectral bands into a range map wherein the value of each "pixel" is the range to the imaged surface. The technique is computationally efficient enough to be performed in real time and does not require specialized illumination or similar restrictive conditions. A calibration procedure is developed which enables the transformation to be practically implemented. An alternate approach to estimating range from multispectral data based on expanding the medium's transfer function and using these terms as elements in sensitivity vectors is also presented and analyzed. Mathematical analysis of the intensity-range transformation and associated developments is provided in terms of its performance in noise and sensitivity to various system parameters. Its performance as a function of light scattering is studied with the aid of computer simulation. Results from transforming actual underwater images are also presented. The results of this analysis and the demonstrated performance of the intensity-range transformation endorse it as a practical enhancement to underwater machine vision systems. / Graduate

One -dimensional infrared speckle scans of Ross 614 AB were recorded at a wavelength of 2.2μm. For each scan an estimate of the instantaneous quality of the seeing was calculated and the scan was binned accordingly. The three bins corresponding to the three best seeing conditions were further processed by applying the shift -and -add algorithm to the set of images contained within each bin, thereby generating three shift- and -add images with differing shift -and -add point -spread- functions. After windowing the shift -and -add images (using edge -extension) to reduce the effect of contamination, we have obtained parameters corresponding to the separation and brightness ratio of a two component model of the double star Ross 614 AB by deconvolving the three shift -and -add images with the aid of the zero-and -add technique. Least squares analysis on the positions of the clusters of zeros found from zero- and -add yields a separation of 1.04 arcseconds and a brightness ratio of 4.3 for the binary system at this wavelength. An extension of the processing, which takes explicit account of the nonlinear motion of the scanning mechanism gives improved estimates of 1.04 arcseconds and 3.9 for the separation and brightness ratio, respectively.

Background and ObjectiveColorectal cancer (CRC) remains the second deadliest cancer in the United States. Several screening methods exist; however, detection of small polyps remains a challenge. Optical coherence tomography (OCT) has been demonstrated to be capable of detecting lesions as small as 1mm in the mouse colon, but detection is based on measuring a doubling of the mucosa thickness. The colon microvasculature may be an attractive biomarker of early tumor development because tumor vessels are characterized by irregular structure and dysfunction. Our goal was to develop an endoscopic method of detecting and segmenting colon vessels using Doppler OCT to enable future studies for improving early detection and development of novel chemopreventive agents. MethodWe conducted in vivo colon imaging in an azoxymethane (AOM)-treated mouse model of colorectal cancer using a miniature endoscope and a swept-source OCT system at 1,040nm with a 16kHz sweep rate. We applied the Kasai autocorrelation algorithm to laterally oversampled OCT B-scans to resolve vascular flow in the mucosa and submucosa. Vessels were segmented by applying a series of image processing steps: (i) intensity thresholding; (ii) two-dimensional matched filtering; and (iii) histogram segmentation. ResultsWe observed differences in the vessels sizes and spatial distribution in a mature adenoma compared to surrounding undiseased tissue and compared the results with histology. We also imaged flow in four young mice (two AOM-treated and two control) showing no significant differences, which is expected so early after carcinogen exposure. We also present flow images of adenoma in a living mouse and a euthanized mouse to demonstrate that no flow is detected after euthanasia. ConclusionWe present, to the best of our knowledge, the first Doppler OCT images of in vivo mouse colon collected with a fiber-based endoscope. We also describe a fast and robust image processing method for segmenting vessels in the colon. These results suggest that Doppler OCT is a promising imaging modality for vascular imaging in the colon that requires no exogenous contrast agents.

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Driving at night is a difficult task. In an attempt to ease this task, most automotive companies are developing systems that aim to increase the safety of the driver and his/her passengers at night. Jaguar Cars Ltd have been involved in such project for several years and have developed a Night Vision System (NVS) based upon the Near Infrared (NIR) and Head-Up Display (HUD) technologies. This thesis is concerned with the application of digital image enhancement algorithms to further increase the driver's visual range at night. The purpose of this research work is to provide the driver with a safe and non-disturbing, enhanced view of the road scene ahead, which is presented on a head-up display. In this automotive environment, specific requirements such as real-time processing, robustness and reliability must be kept in mind to design algorithms that will not compromise the safety of the driver, his/her passengers and other road users. To fulfill these requirements, we have developed a novel intelligent image enhancement scheme for night time driving that actively adapts to the road scene. This scheme results in the enhancement of the contrast in a portion of the projected HUD road scene as if extra headlamps were directed to the region of the image that represents where the road is going. Human Factors studies have shown that this region is where the driver is concentrating his attention when driving. The position of the region of interest is defined by the computation of an approximation of the vanishing point of the road, updated for every frame using a novel, reliable and optimised road edge detection algorithm. The enhancement of the contrast within the region of interest is obtained by applying several novel low-level algorithms based upon the grey level segmentation of the image into regions and the use of the global histogram equalisation and quantised bi-histogram equalisation algorithms. These novel algorithms have all been implemented on the Matrox Genesis board based upon the multitasking, multiprocessor and parallel DSP TMS320C80 chip from Texas Instruments. All algorithms described in this thesis are able to sustain real-time processing at the NTSC frame rate of 30 frames per second. This new concept for a night time driving aid is an attractive solution that meets the numerous requirements driven by Human Factors research in an automotive environment, in particular safety requirements.

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Previous studies of the induced current impedance imaging technique have been unable to reconstruct images of three dimensional (3-D) structures. In this study the cause of the problem is identified and the reconstruction algorithm of Purvis is adapted to facilitate the correct reconstruction of images of a limited class of structures which have the form of a long cylinder. The images produced by the algorithm are improved by a data filter based on that of Barber, Brown and Avis. By consideration of the underlying field equations which govern 3-D induced current Electrical Impedance Tomography (EIT) systems, the finite element method (FEM) is used for the computation of the potential field for arbitrary conductivity distributions excited by various coil configurations. A phantom system is built to test the results of the FEM and particular attention is paid to the improvement of the instrumentation. A statistical comparison of the results of measurement and simulation is unable to detect any error in the FEM model. The FEM model is consequently used to develop the reconstruction algorithm but physical measurements are also used to test the algorithm in the presence of noise. The behaviour of the 3-D algorithm is tested for its plane selectivity showing Similar characteristics to those of injected current systems developed by other workers. A possible approach which could both reduce the volume to which the system is sensitive and generate extra measurements for the possible reconstruction of multi-layered images is investigated.

The computational demands of real-time image processing often dictate the use of techniques such as parallel processing to meet required performance. This thesis considers a range of technology which may be used to accelerate image processing operations. An occam compiler is ported to a PowerPC based parallel computer. A multiprocessor configuration tool and Run Time System is developed, allowing occam programs to be distributed over an arbitrary sized network of PowerPC microprocessors. Code optimization techniques for image processing operations are investigated, with the development of a post-compilation code optimizer. The optimizer provides performance increases between 37% and 450% for a variety of image processing algorithms. The applicability of these tools is demonstrated with two image processing applications, micro-biological rapid imaging and sediment texture analysis. Edge detection, region merging and shape analysis algorithms are discussed in the context of the applications. The image processing algorithms are implemented in occam and performance is compared on serial and parallel platforms. The algorithms are then ported to a hardware implementation in a custom computing device, based on a field programmable gate array (FPGA), using the Handel hardware compilation system. The issues involved with this porting are discussed, including the compromises which must be considered when designing for a size constrained hardware platform. Amongst the issues considered are restricted precision data, low level parallelism and algorithmic simplifications. To provide performance equivalent to the hardware, between 5 and 10 processors would be required on the parallel machine, with considerably greater cost, size and power consumption.