The aim of this work was to implement a thorough method for quantifying the errors introduced to frame-based neurosurgical stereotactic procedures by the use of MRI. Chang & Fitzpatrick's reversed gradient distortion correction method was used, in combination with a phantom, to measure these errors. Spatial distortion in MR images of between 1 mm and 2 mm was measured. Further analysis showed that this typically introduced an additional error in the coordinate of the actual treatment point of 0.7 mm. The implications of this are discussed. The main source of distortion in the MR images used for stereotaxis was found to be the head ring. A comparison between imaging sequences and MR scanners revealed that the spatial distortion depends mainly on the bandwidth per pixel of the sequence rather than other differences in the imaging sequences. By comparison with a phase map distortion correction technique, the imaging parameters required to allow successful distortion correction with the reversed gradient method were identified. The most important was the use of full Fourier spin echo acquisitions. The reversed gradient correction method was applied to two contemporary EPI techniques. Considerable improvement was seen in the production of ADC maps after the images had been corrected for distortion. The method also was shown to be valid in application to BOLD fMRI data.

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.

No description available.

This thesis investigates a novel scene analysis system that determines the identity and the relative positions of unconstrained objects within a natural three dimensional grey scale image. Images may be of 'block filled' or 'line drawn' occluded shapes. It utilises the occluding information to discover the relative depth of objects in the scene. The system incorporates associative memories, the N tuple pattern recognition process, movable multiple resolution windows and edge detection. The structure and performance of the system and its subsystems is reported. The associative memory incorporates a novel recall procedure which has uses outside the application given here. The work incorporates ideas from the neurophysiology of the human visual system to overcome some of the problems encountered.

An extensive literature survey has revealed that the majority of previous work in infrared image processing has ignored the processes leading to the formation of infrared images. Processing has normally either been restricted to simple lowlevel image enhancement convolutions or has consisted of algorithms copied from computer vision without regard for the inherent differences between infrared and visible images. In this thesis, we address the problem of infrared image formation and derive an irradiance equation for simple infrared scenes. We consider the complications caused by mutual illumination of one or more bodies and indicate how the infrared irradiance equation can also be specified for more complex scenes. The infrared irradiance equation we derive is solved in closed form for some simple geometries for both Lambertian and non-Lambertian surfaces. An infrared imager has been built and is described. Images taken with the imager of a variety of scene geometries show that the experimental results compare favourably with the theoretically derived equations, indicating the validity of the theoretical analysis. We describe how a knowledge of the formation of infrared images can be used to predict the image irradiance pattern of a particular object. We also show how, with a knowledge of the radiance properties and surface geometry of the object, it is possible to detect instances of that object in a scene. Examples are given of successful object detection based on an understanding of the image irradiance. We present a brief history of infrared imagers and a description of the principles on which modern infrared imagers are based. In addition to the survey of the literature published on infrared image processing, a brief summary of some techniques from the computer vision literature and their suitability to infrared image processing is given. A selection of vision techniques are applied to both infrared and visible images to verify conclusions reached in the thesis.

This thesis describes research on recognizing partially obscured objects using surface information like Marr's 2D sketch ([MAR82]) and surface-based geometrical object models. The goal of the recognition process is to produce a fully instantiated object hypotheses, with either image evidence for each feature or explanations for their absence, in terms of self or external occlusion. The central point of the thesis is that using surface information should be an important part of the image understanding process. This is because surfaces are the features that directly link perception to the objects perceived (for normal "camera-like" sensing) and because surfaces make explicit information needed to understand and cope with some visual problems (e.g. obscured features). Further, because surfaces are both the data and model primitive, detailed recognition can be made both simpler and more complete. Recognition input is a surface image, which represents surface orientation and absolute depth. Segmentation criteria are proposed for forming surface patches with constant curvature character, based on surface shape discontinuities which become labeled segmentation- boundaries. Partially obscured object surfaces are reconstructed using stronger surface based constraints. Surfaces are grouped to form surface clusters, which are 3D identity-independent solids that often correspond to model primitives. These are used here as a context within which to select models and find all object features. True three-dimensional properties of image boundaries, surfaces and surface clusters are directly estimated using the surface data. Models are invoked using a network formulation, where individual nodes represent potential identities for image structures. The links between nodes are defined by generic and structural relationships. They define indirect evidence relationships for an identity. Direct evidence for the identities comes from the data properties. A plausibility computation is defined according to the constraints inherent in the evidence types. When a node acquires sufficient plausibility, the model is invoked for the corresponding image structure.Objects are primarily represented using a surface-based geometrical model. Assemblies are formed from subassemblies and surface primitives, which are defined using surface shape and boundaries. Variable affixments between assemblies allow flexibly connected objects. The initial object reference frame is estimated from model-data surface relationships, using correspondences suggested by invocation. With the reference frame, back-facing, tangential, partially self-obscured, totally self-obscured and fully visible image features are deduced. From these, the oriented model is used for finding evidence for missing visible model features. IT no evidence is found, the program attempts to find evidence to justify the features obscured by an unrelated object. Structured objects are constructed using a hierarchical synthesis process. Fully completed hypotheses are verified using both existence and identity constraints based on surface evidence. Each of these processes is defined by its computational constraints and are demonstrated on two test images. These test scenes are interesting because they contain partially and fully obscured object features, a variety of surface and solid types and flexibly connected objects. All modeled objects were fully identified and analyzed to the level represented in their models and were also acceptably spatially located. Portions of this work have been reported elsewhere ([FIS83], [FIS85a], [FIS85b], [FIS86]) by the author.

This thesis examines the use of multi-spectral remotely sensed data in the management of the North York Moors, an upland area of heather moorland in northern England. A series of ground radiometer surveys and airborne simulations are analysed to determine the relative importance of spatial, spectral and temporal resolution as characteristics of earth resources satellites in this environment. Particular reference is made to the potential for selecting and combining data from the Landsat MSS, TM and the SPOT HRV sensors. The results show that spectral resolution can be critical in isolating and recognising elements of the moorland community by their spectral response, especially at the most detailed levels of vegetational description. Temporal resolution has little effect on the discrimination of targets within the heather dominated areas but affects the separability of the major communities of heather, bracken and sedges. Change in spatial resolution has no clear effect on the spectral uniformity and spectral separation of the elements of the heather dominated areas. The interaction between spectral and spatial resolution is more important in isolating the major communities, where the requirement for spatial precision is balanced against the need to suppress spectral variation within the moorland. The hypothesis that multi-spectral remotely sensed data can provide critical information on the distribution and status of moorland vegetation is not refuted in this thesis. Remotely sensed data would make the greatest contribution to management if linked to other spatial data as part of a Geographical Information System. In the absence of such a formal structure satellite imagery can still provide a regular and unique inventory of the moorland habitat which will increase the efficiency of management.

No description available.

The research described in this thesis addresses aspects of coding of discrete-cosinetransform (DCT) coefficients, that are present in a variety of transform-based digital-image-compression schemes such as JPEG. Coefficient reordering; that directly affects the symbol statistics for entropy coding, and therefore the effectiveness of entropy coding; is investigated. Adaptive zigzag reordering, a novel versatile technique that achieves efficient reordering by processing variable-size rectangular sub-blocks of coefficients, is developed. Classification of blocks of DCT coefficients using an artificial neural network (ANN) prior to adaptive zigzag reordering is also considered. Some established digital-image-compression techniques are reviewed, and the JPEG standard for the DCT-based method is studied in more detail. An introduction to artificial neural networks is provided. Lossless conversion of blocks of coefficients using adaptive zigzag reordering is investigated, and experimental results are presented. A versatile algorithm, that generates zigzag scan paths for sub-blocks of any dimensions using a binary decision tree, is developed. An implementation of the algorithm based on programmable logic devices (PLDs) is described demonstrating the feasibility of hardware implementations. Coding of the sub-block dimensions, that need to be retained in order to reconstruct a sub-block during decoding, based on the scan-path length is developed. Lossy conversion of blocks of coefficients is also considered, and experimental results are presented. A two-layer feedforward artificial neural network trained using an error-backpropagation algorithm, that determines the sub-block dimensions, is described. Isolated nonzero coefficients of small significance are discarded in some blocks, and therefore smaller sub-blocks are generated.