For many digital image/video processing applications increasing the spatial resolution is highly beneficial. At higher resolution, TV pictures look more natural and pleasing to the eye, computer vision tasks such as object detection and tracking can be performed with higher precision, medical diagnoses can be made with a higher confidence, security cameras can offer better identification, and satellite imagery can be interpreted with higher accuracy. As such, spatial resolution is an influential parameter in many mainstream imaging applications, and resolution enhancement task naturally arises as a means of increasing the effectiveness of any imaging system used in the mentioned applications. In this thesis, we concentrate on two enhancement problems of practical importance, namely, low-complexity resolution enhancement for customer grade flat panel televisions, and resolution enhancement of noisy high-dimensional hyperspectral imagery. For TV resolution enhancement our main concern is keeping computational complexity at a minimum. The hardware limitations of average customer grade televisions effectively rule out a multi-frame approach. Hence, we take a low-complexity single-frame approach based on exploiting natural image characteristics. For hyperspectral imagery we take advantage of multiple observations in a modified superresolution framework. Here the main challenges are the high dimensionality of hyperspectral data and the noise present in all spectral bands. We design a physical model of the hyperspectral image acquisition process, and based on this model we formulate an iterative resolution enhancement algorithm.

<p>Much work has been done in building systems that can recognize gestures, e.g. as a component of sign language recognition systems. These systems typically use whole gestures as the smallest unit for recognition. Although high recognition rates have been reported, these systems do not scale well and are computationally intensive. The reason why these systems generally scale poorly is that they recognize gestures by building individual models for each separate gesture / as the number of gestures grows, so does the required number of models. Beyond a certain threshold number of gestures to be recognized, this approach become infeasible. This work proposed that similarly good recognition rates can be achieved by building models for subcomponents of whole gestures, so-called cheremes. Instead of building models for entire gestures, we build models for cheremes and recognize gestures as sequences of such cheremes. The assumption is that many gestures share cheremes and that the number of cheremes necessary to describe gestures is much smaller than the number of gestures. This small number of cheremes then makes it possible to recognized a large number of gestures with a small number of chereme models. This approach is akin to phoneme-based speech recognition systems where utterances are recognized as phonemes which in turn are combined into words.</p>

Mammography is currently used to screen women in targeted risk classes for breast cancer. Computer assisted diagnosis of mammograms attempts to lower the workload on radiologists by either automating some of their tasks or acting as a second reader. The task of mammogram segmentation based on pixel intensity is addressed in this thesis. The mammographic process leads to images where intensity in the image is related to the composition of tissue in the breast; it is therefore possible to segment a mammogram into several regions using a combination of global thresholds, local thresholds and higher-level information based on the intensity histogram. A hierarchical view is taken of the segmentation process, with a series of steps that feed into each other. Methods are presented for segmentation of: 1. image background regions; 2. skin-air interface; 3. pectoral muscle; and 4. segmentation of the database by classification of mammograms into tissue types and determining a similarity measure between mammograms. All methods are automatic. After a detailed analysis of minimum cross-entropy thresholding, multi-level thresholding is used to segment the main breast tissue from the background. Scanning artefacts and high intensity noise are separated from the breast tissue using binary image operations, rectangular labels are identified from the binary image by their shape, the Radon transform is used to locate the edges of tape artefacts, and a filter is used to locate vertical running roller scratching. Orientation of the image is determined using the shape of the breast and properties of the breast tissue near the breast edge. Unlike most existing orientation algorithms, which only distinguish between left facing or right facing breasts, the algorithm developed determines orientation for images flipped upside down or rotated onto their side and works successfully on all images of the testing database. Orientation is an integral part of the segmentation process, as skin-air interface and pectoral muscle extraction rely on it. A novel way to view the skin-line on the mammogram is as two sets of functions, one set with the x-axis along the rows, and the other with the x-axis along the columns. Using this view, a local thresholding algorithm, and a more sophisticated optimisation based algorithm are presented. Using fitted polynomials along the skin-air interface, the error between polynomial and breast boundary extracted by a threshold is minimised by optimising the threshold and the degree of the polynomial. The final fitted line exhibits the inherent smoothness of the polynomial and provides a more accurate estimate of the skin-line when compared to another established technique. The edge of the pectoral muscle is a boundary between two relatively homogenous regions. A new algorithm is developed to obtain a threshold to separate adjacent regions distinguishable by intensity. Taking several local windows containing different proportions of the two regions, the threshold is found by examining the behaviour of either the median intensity or a modified cross-entropy intensity as the proportion changes. Image orientation is used to anchor the window corner in the pectoral muscle corner of the image and straight-line fitting is used to generate a more accurate result from the final threshold. An algorithm is also presented to evaluate the accuracy of different pectoral edge estimates. Identification of the image background and the pectoral muscle allows the breast tissue to be isolated in the mammogram. The density and pattern of the breast tissue is correlated with 1. Breast cancer risk, and 2. Difficulty of reading for the radiologist. Computerised density assessment methods have in the past been feature-based, a number of features extracted from the tissue or its histogram and used as input into a classifier. Here, histogram distance measures have been used to classify mammograms into density types, and ii also to order the image database according to image similarity. The advantage of histogram distance measures is that they are less reliant on the accuracy of segmentation and the quality of extracted features, as the whole histogram is used to determine distance, rather than quantifying it into a set of features. Existing histogram distance measures have been applied, and a new histogram distance presented, showing higher accuracy than other such measures, and also better performance than an established feature-based technique.

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pt.1. Performance analysis--pt.2. Robustness analysis. / David J. Rossi, Alan S. Willsky. / Includes bibliographies. / Caption title. "13 July 1983." / National Science Foundation grant ECS-8012668

David J. Rossi, Alan S. Willsky. / Caption title. "Presented at the 1983 International Conference on Acoustics, Speech and Signal Processing." / Bibliography: leaf [3] / National Science Foundation Grant ECS-8012668

Thesis (MScEng)--University of Stellenbosch, 2005. / ENGLISH ABSTRACT: This thesis documents the research, design, implementation and successful testing of a prototype camera probe to survey the inside of hard rock boreholes. Rock core images are intended to aid mine geologists in recording the borehole rock layers. The system consists of a wide-angle fisheye lens mounted onto a CMOS digital image sensor. The image data is read in and processed by an FPGA, then stored on a removable sn flash memory card. All of the aforementioned components are mounted inside a watertight Perspex tube. Application specific PC software is used to process the data to form strip images of the borehole wall. Using mathematical correlation, these images are stitched together into a virtual core that is a flattened representation of the rock inside the borehole. The probe contains its own power and light source which enables it to be deployed easily with no external wires needed for operation. The storage capacity, image quality, and lighting design can be improved in future design revisions. / AFRIKAANSE OPSOMMING: Die inhoud van hierdie tesis behels die navorsmg, ontwerp, implementering, en suksesvolle toetsing van 'n prototipe kameraprobe wat dit moontlik maak om die binnewand van boorgate in harde rots te besigtig. Beelde van die rotskern vergemaklik die taak van myngeoloë wat die rotslae, waardeur die boorgat strek, moet opteken. Die stelsel behels 'n wyehoek bollens wat op 'n CMOS digitale sensor gemonteer is. Die data gewerf vir die vorming van die beeld word deur 'n FPGA ingelees en verwerk, waarna dit op 'n verwyderbare SD flits geheuekaart gestoor word. Die bogenoemde komponente word alles binne 'n waterdigte Perspexbuis monteer. Gebruikerspesifieke sagteware vir persoonlik rekenaars word gebruik om die data te verwerk en sodoende strookbeelde van die binnewand van die boorgat te vorm. Met gebruik van wiskundige korrelasie word hierdie beelde aan mekaar gelas om 'n virtuele kern te vorm, wat 'n voorstelling is van die rots binne die boorgat. Die probe bevat self krag en ligbronne, wat toelaat dat dit maklik bruikbaar is sonder enige eksterne bedrading. Toekomstige hersienings van die ontwerp sal verbeterde data geheue, beeldgehalte en beligting kan bewerkstellig.