Fetal Alcohol Spectrum Disorder (FASD) is the most common form of preventable mental retardation worldwide. This condition affects children whose mothers excessively consume alcohol whilst pregnant. FASD can be identied by physical and mental defects, such as stunted growth, facial deformities, cognitive impairment, and behavioural abnormalities. Magnetic Resonance Imaging provides a non-invasive means to study the neural correlates of FASD. One such approach aims to detect brain abnormalities through an assessment of volume and shape of sub-cortical structures on high-resolution MR images. Two brain structures of interest are the Caudate Nucleus and Hippocampus. Manual segmentation of these structures is time-consuming and subjective. We therefore present a method for automatically segmenting the Caudate Nucleus and Hippocampus from high-resolution MR images captured as part of an ongoing study into the neural correlates of FASD. Our method incorporates an Active Shape Model (ASM), which is used to learn shape variation from manually segmented training data. A discrete Geometrically Deformable Model (GDM) is rst deformed to t the relevant structure in each training set. The vertices belonging to each GDM are then used as 3D landmark points - effectively generating point correspondence between training models. An ASM is then created from the landmark points. This ASM is only able to deform to t structures with similar shape to those found in the training data. There are many variations of the standard ASM technique - each suited to the segmentation of data with particular characteristics. Experiments were conducted on the image search phase of ASM segmentation, in order to find the technique best suited to segmentation of the research data. Various popular image search techniques were tested, including an edge detection method and a method based on grey prole Mahalanobis distance measurement. A heuristic image search method, especially designed to target Caudate Nuclei and Hippocampi, was also developed and tested. This method was extended to include multisampling of voxel proles. ASM segmentation quality was evaluated according to various quantitative metrics, including: overlap, false positives, false negatives, mean squared distance and Hausdorff distance. Results show that ASMs that use the heuristic image search technique, without multisampling, produce the most accurate segmentations. Mean overlap for segmentation of the various target structures ranged from 0.76 to 0.82. Mean squared distance ranged from 0.72 to 0.76 - indicating sub-1mm accuracy, on average. Mean Hausdorff distance ranged from 2:7mm to 3:1mm. An ASM constructed using our heuristic technique will enable researchers to quickly, reliably, and automatically segment test data for use in the FASD study - thereby facilitating a better understanding of the eects of this unfortunate condition.

The 3D shape and position of objects inside the human body are commonly detected using Computed Tomography (CT) scanning. CT is an expensive diagnostic option in economically disadvantaged areas and the radiation dose experienced by the patient is significant. In this dissertation, we present a technique for reconstructing the 3D shape and position of bullets from multiple X-rays. This technique makes us of ubiquitous X-ray equipment and a small number of X-rays to reduce the radiation dose. Our work relies on Image Segmentation and Volume Reconstruction techniques. We present a method for segmenting bullets out of X-rays, based on their signature in intensity profiles. This signature takes the form of a distinct plateau which we model with a number of parameters. This model is used to identify horizontal and vertical line segments within an X-Ray corresponding to a bullet signature. Regions containing confluences of these line segments are selected as bullet candidates. The actual bullet is thresholded out of the region based on a range of intensities occupied by the intensity profiles that contributed to the region. A simple Volume Reconstruction algorithm is implemented that back-projects the silhouettes of bullets obtained from our segmentation technique. This algorithm operates on a 3D voxel volume represented as an octree. The reconstruction is reduced to the 2D case by reconstructing a slice of the voxel volume at a time. We achieve good results for our segmentation algorithm. When compared with a manual segmentation, our algorithm matches 90% of the bullet pixels in nine of the twelve test X-rays. Our reconstruction algorithm produces an acceptable results: It achieves a 70% match for a test case where we compare a simulated bullet with a reconstructed bullet.

World wide focus has over the years been shifting towards security issues, not in least due to recent world wide terrorist activities. Several researchers have proposed state of the art surveillance systems to help with some of the security issues with varying success. Recent studies have suggested that the ability of these surveillance systems to learn common environmental behaviour patterns as wells as to detect and predict unusual, or anomalous, activities based on those learnt patterns are possible improvements to those systems. In addition, some of these surveillance systems are still run by human operators, who are prone to mistakes and may need some help from the surveillance systems themselves in detection of anomalous activities. This dissertation attempts to address these suggestions by combining the fields of Image Understanding and Artificial Intelligence, specifically Bayesian Networks, to develop a prototype video surveillance system that can learn common environmental behaviour patterns, thus being able to detect and predict anomalous activity in the environment based on those learnt patterns. In addition, this dissertation aims to show how the prototype system can adapt to these anomalous behaviours and integrate them into its common patterns over a prolonged occurrence period. The prototype video surveillance system showed good performance and ability to detect, predict and integrate anomalous activity in the evaluation tests that were performed using a volunteer in an experimental indoor environment. In addition, the prototype system performed quite well on the PETS 2002 dataset 1, which it was not designed for. The evaluation procedure used some of the evaluation metrics commonly used on the PETS datasets. Hence, the prototype system provides a good approach to anomaly detection and prediction using Bayesian Networks trained on common environmental activities.

This thesis presents a complete self-calibrating illumination intensity-invariant colour classification system. We extend a novel fuzzy colour processing tech- nique called Fuzzy Colour Contrast Fusion (FCCF) by combining it with a Heuristic- assisted Genetic Algorithm (HAGA) for automatic fine-tuning of colour descriptors. Furthermore, we have improved FCCF’s efficiency by processing colour channels at varying colour depths in search for the optimal ones. In line with this, we intro- duce a reduced colour depth representation of a colour image while maintaining efficient colour sensitivity that suffices for accurate real-time colour-based object recognition. We call the algorithm Variable Colour Depth (VCD) and we propose a technique for building and searching a VCD look-up table (LUT). The first part of this work investigates the effects of applying fuzzy colour contrast rules to vary- ing colour depths as we extract the optimal rule combination for any given target colour exposed under changing illumination intensities. The second part introduces the HAGA-based parameter-optimisation for automatically constructing accurate colour classifiers. Our results show that for all cases, the VCD algorithm, combined with HAGA for parameter optimisation improve colour classification via a pie-slice colour classifier.For 6 different target colours, the hybrid algorithm was able to yield 17.63% higher overall accuracy as compared to the pure fuzzy approach. Fur- thermore, it was able to reduce LUT storage space by 78.06% as compared to the full-colour depth LUT.

This thesis studies rapid object detection, focusing on feature-based methods. Firstly, modifications of training and detection of the Viola-Jones method are made to improve performance and overcome some of the current limitations such as rotation, occlusion and articulation. New classifiers produced by training and by converting existing classifiers are tested in face detection and hand detection. Secondly, the nature of invariant features in terms of the computational complexity, discrimination power and invariance to rotation and scaling are discussed. A new feature extraction method called Concentric Discs Moment Invariants (CDMI) is developed based on moment invariants and summed-area tables. The dimensionality of this set of features can be increased by using additional concentric discs, rather than using higher order moments. The CDMI set has useful properties, such as speed, rotation invariance, scaling invariance, and rapid contrast stretching can be easily implemented. The results of experiments with face detection shows a clear improvement in accuracy and performance of the CDMI method compared to the standard moment invariants method. Both the CDMI and its variant, using central moments from concentric squares, are used to assess the strength of the method applied to hand-written digits recognition. Finally, the parallelisation of the detection algorithm is discussed. A new model for the specific case of the Viola-Jones method is proposed and tested experimentally. This model takes advantage of the structure of classifiers and of the multi-resolution approach associated with the detection method. The model shows that high speedups can be achieved by broadcasting frames and carrying out the computation of one or more cascades in each node.

Digital colour cameras are dramatically falling in price, making them a ordable for ubiquitous appliances in many applications. Change in colour perception with changing light conditions induce errors that may escape a user's awareness. Colour constancy algorithms are based on inferring light properties (usually the white point) to correct colour. Other attempts using more data for colour correction such as (ICC based) colour management characterise a capturing device under given conditions through an input device pro le. This pro le can be applied to correct for deviating colour perception. But this pro le is only valid for the speci c conditions at the time of the characterisation, but fails with changes in light. This research presents a solution to the problem of long time observations with changes in the scene's illumination for common natural (overcast or clear, blue sky) and arti cial sources (incandescent or uorescent lamps). Colour measurements for colour based reasoning need to be represented in a robustly de ned way. One such suitable and well de ned description is given by the CIE LAB colour space, a device-independent, visually linearised colour description. Colour transformations using ICC pro le are also based on CIE colour descriptions. Therefore, also the corrective colour processing has been based on ICC based colour management. To verify the viability of CIE LAB based corrective colour processing colour constancy algorithms (White Patch Retinex and Grey World Assumption) have been modi ed to operate on L a b colour tuples. Results were compared visually and numerically (using colour indexing) against those using the same algorithms operating on RGB colour tuples. We can take advantage of the fact that we are dealing with image streams over time, adding another dimension usable for analysis. A solution to the problem of slowly changing light conditions in scenes with a static camera perspective is presented. It takes advantage of the small (frame-to-frame) changes in appearance of colour within the scene over time. Reoccurring objects or (background) areas of the scene are tracked to gather data points for an analysis. As a result, a suitable colour space distortion model has been devised through a rst order Taylor approximation (a ne transformation). By performing a multidimensional linear regression analysis on the tracked data points, parameterisations for the a ne transformations were derived. Finally, the device pro le is updated by amalgamating the corrections from the model into the ICC pro le for a single, comprehensive transformation. Following applications of the ICC based colour pro les are very fast and can be used in real-time with the camera's capturing frame rate (for current normal web cameras and low spec desktop computers). As light conditions usually change on a much slower time scale than the capturing rate of a camera, the computationally expensive pro le adaptation generally showed to be usable for many frames. The goal was to set out and nd a solution for consistent colour capturing using digital cameras, which is capable of coping with changing light conditions. Theoretical backgrounds and strategies for such a system have been devised and implemented successfully.

Digital colour cameras are dramatically falling in price, making them a ordable for ubiquitous appliances in many applications. Change in colour perception with changing light conditions induce errors that may escape a user's awareness. Colour constancy algorithms are based on inferring light properties (usually the white point) to correct colour. Other attempts using more data for colour correction such as (ICC based) colour management characterise a capturing device under given conditions through an input device pro le. This pro le can be applied to correct for deviating colour perception. But this pro le is only valid for the speci c conditions at the time of the characterisation, but fails with changes in light. This research presents a solution to the problem of long time observations with changes in the scene's illumination for common natural (overcast or clear, blue sky) and arti cial sources (incandescent or uorescent lamps). Colour measurements for colour based reasoning need to be represented in a robustly de ned way. One such suitable and well de ned description is given by the CIE LAB colour space, a device-independent, visually linearised colour description. Colour transformations using ICC pro le are also based on CIE colour descriptions. Therefore, also the corrective colour processing has been based on ICC based colour management. To verify the viability of CIE LAB based corrective colour processing colour constancy algorithms (White Patch Retinex and Grey World Assumption) have been modi ed to operate on L a b colour tuples. Results were compared visually and numerically (using colour indexing) against those using the same algorithms operating on RGB colour tuples. We can take advantage of the fact that we are dealing with image streams over time, adding another dimension usable for analysis. A solution to the problem of slowly changing light conditions in scenes with a static camera perspective is presented. It takes advantage of the small (frame-to-frame) changes in appearance of colour within the scene over time. Reoccurring objects or (background) areas of the scene are tracked to gather data points for an analysis. As a result, a suitable colour space distortion model has been devised through a rst order Taylor approximation (a ne transformation). By performing a multidimensional linear regression analysis on the tracked data points, parameterisations for the a ne transformations were derived. Finally, the device pro le is updated by amalgamating the corrections from the model into the ICC pro le for a single, comprehensive transformation. Following applications of the ICC based colour pro les are very fast and can be used in real-time with the camera's capturing frame rate (for current normal web cameras and low spec desktop computers). As light conditions usually change on a much slower time scale than the capturing rate of a camera, the computationally expensive pro le adaptation generally showed to be usable for many frames. The goal was to set out and nd a solution for consistent colour capturing using digital cameras, which is capable of coping with changing light conditions. Theoretical backgrounds and strategies for such a system have been devised and implemented successfully.

Digital colour cameras are dramatically falling in price, making them a ordable for ubiquitous appliances in many applications. Change in colour perception with changing light conditions induce errors that may escape a user's awareness. Colour constancy algorithms are based on inferring light properties (usually the white point) to correct colour. Other attempts using more data for colour correction such as (ICC based) colour management characterise a capturing device under given conditions through an input device pro le. This pro le can be applied to correct for deviating colour perception. But this pro le is only valid for the speci c conditions at the time of the characterisation, but fails with changes in light. This research presents a solution to the problem of long time observations with changes in the scene's illumination for common natural (overcast or clear, blue sky) and arti cial sources (incandescent or uorescent lamps). Colour measurements for colour based reasoning need to be represented in a robustly de ned way. One such suitable and well de ned description is given by the CIE LAB colour space, a device-independent, visually linearised colour description. Colour transformations using ICC pro le are also based on CIE colour descriptions. Therefore, also the corrective colour processing has been based on ICC based colour management. To verify the viability of CIE LAB based corrective colour processing colour constancy algorithms (White Patch Retinex and Grey World Assumption) have been modi ed to operate on L a b colour tuples. Results were compared visually and numerically (using colour indexing) against those using the same algorithms operating on RGB colour tuples. We can take advantage of the fact that we are dealing with image streams over time, adding another dimension usable for analysis. A solution to the problem of slowly changing light conditions in scenes with a static camera perspective is presented. It takes advantage of the small (frame-to-frame) changes in appearance of colour within the scene over time. Reoccurring objects or (background) areas of the scene are tracked to gather data points for an analysis. As a result, a suitable colour space distortion model has been devised through a rst order Taylor approximation (a ne transformation). By performing a multidimensional linear regression analysis on the tracked data points, parameterisations for the a ne transformations were derived. Finally, the device pro le is updated by amalgamating the corrections from the model into the ICC pro le for a single, comprehensive transformation. Following applications of the ICC based colour pro les are very fast and can be used in real-time with the camera's capturing frame rate (for current normal web cameras and low spec desktop computers). As light conditions usually change on a much slower time scale than the capturing rate of a camera, the computationally expensive pro le adaptation generally showed to be usable for many frames. The goal was to set out and nd a solution for consistent colour capturing using digital cameras, which is capable of coping with changing light conditions. Theoretical backgrounds and strategies for such a system have been devised and implemented successfully.

Digital colour cameras are dramatically falling in price, making them a ordable for ubiquitous appliances in many applications. Change in colour perception with changing light conditions induce errors that may escape a user's awareness. Colour constancy algorithms are based on inferring light properties (usually the white point) to correct colour. Other attempts using more data for colour correction such as (ICC based) colour management characterise a capturing device under given conditions through an input device pro le. This pro le can be applied to correct for deviating colour perception. But this pro le is only valid for the speci c conditions at the time of the characterisation, but fails with changes in light. This research presents a solution to the problem of long time observations with changes in the scene's illumination for common natural (overcast or clear, blue sky) and arti cial sources (incandescent or uorescent lamps). Colour measurements for colour based reasoning need to be represented in a robustly de ned way. One such suitable and well de ned description is given by the CIE LAB colour space, a device-independent, visually linearised colour description. Colour transformations using ICC pro le are also based on CIE colour descriptions. Therefore, also the corrective colour processing has been based on ICC based colour management. To verify the viability of CIE LAB based corrective colour processing colour constancy algorithms (White Patch Retinex and Grey World Assumption) have been modi ed to operate on L a b colour tuples. Results were compared visually and numerically (using colour indexing) against those using the same algorithms operating on RGB colour tuples. We can take advantage of the fact that we are dealing with image streams over time, adding another dimension usable for analysis. A solution to the problem of slowly changing light conditions in scenes with a static camera perspective is presented. It takes advantage of the small (frame-to-frame) changes in appearance of colour within the scene over time. Reoccurring objects or (background) areas of the scene are tracked to gather data points for an analysis. As a result, a suitable colour space distortion model has been devised through a rst order Taylor approximation (a ne transformation). By performing a multidimensional linear regression analysis on the tracked data points, parameterisations for the a ne transformations were derived. Finally, the device pro le is updated by amalgamating the corrections from the model into the ICC pro le for a single, comprehensive transformation. Following applications of the ICC based colour pro les are very fast and can be used in real-time with the camera's capturing frame rate (for current normal web cameras and low spec desktop computers). As light conditions usually change on a much slower time scale than the capturing rate of a camera, the computationally expensive pro le adaptation generally showed to be usable for many frames. The goal was to set out and nd a solution for consistent colour capturing using digital cameras, which is capable of coping with changing light conditions. Theoretical backgrounds and strategies for such a system have been devised and implemented successfully.

Digital colour cameras are dramatically falling in price, making them a ordable for ubiquitous appliances in many applications. Change in colour perception with changing light conditions induce errors that may escape a user's awareness. Colour constancy algorithms are based on inferring light properties (usually the white point) to correct colour. Other attempts using more data for colour correction such as (ICC based) colour management characterise a capturing device under given conditions through an input device pro le. This pro le can be applied to correct for deviating colour perception. But this pro le is only valid for the speci c conditions at the time of the characterisation, but fails with changes in light. This research presents a solution to the problem of long time observations with changes in the scene's illumination for common natural (overcast or clear, blue sky) and arti cial sources (incandescent or uorescent lamps). Colour measurements for colour based reasoning need to be represented in a robustly de ned way. One such suitable and well de ned description is given by the CIE LAB colour space, a device-independent, visually linearised colour description. Colour transformations using ICC pro le are also based on CIE colour descriptions. Therefore, also the corrective colour processing has been based on ICC based colour management. To verify the viability of CIE LAB based corrective colour processing colour constancy algorithms (White Patch Retinex and Grey World Assumption) have been modi ed to operate on L a b colour tuples. Results were compared visually and numerically (using colour indexing) against those using the same algorithms operating on RGB colour tuples. We can take advantage of the fact that we are dealing with image streams over time, adding another dimension usable for analysis. A solution to the problem of slowly changing light conditions in scenes with a static camera perspective is presented. It takes advantage of the small (frame-to-frame) changes in appearance of colour within the scene over time. Reoccurring objects or (background) areas of the scene are tracked to gather data points for an analysis. As a result, a suitable colour space distortion model has been devised through a rst order Taylor approximation (a ne transformation). By performing a multidimensional linear regression analysis on the tracked data points, parameterisations for the a ne transformations were derived. Finally, the device pro le is updated by amalgamating the corrections from the model into the ICC pro le for a single, comprehensive transformation. Following applications of the ICC based colour pro les are very fast and can be used in real-time with the camera's capturing frame rate (for current normal web cameras and low spec desktop computers). As light conditions usually change on a much slower time scale than the capturing rate of a camera, the computationally expensive pro le adaptation generally showed to be usable for many frames. The goal was to set out and nd a solution for consistent colour capturing using digital cameras, which is capable of coping with changing light conditions. Theoretical backgrounds and strategies for such a system have been devised and implemented successfully.