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Several medical imaging techniques are capable of producing tomographic images, corresponding to cross-sections through the body. A stack of adjacent sections contains three dimensional information about the organs of interest, and this can be presented on a two dimensional screen using shaded surface techniques. In order to facilitate the routine use of such images, algorithms and techniques were developed on a conventional medical imaging computer system in a hospital environment. Several object representation schemes were compared, and two new schemes were devised. The 'solid binary object' technique facilitated exploration of the interior of an object, while the 'ordered surface list' technique enabled real time display of object surfaces. Several shading algorithms were compared, and a local polynomial fitting routine was devised. This was found to be superior to other methods using objective evaluation of the accuracy of surface normal estimations, and subjective evaluation of the corresponding image appearance. The techniques developed were applied to a variety of data obtained using xray computed tomography, nuclear magnetic resonance and emission computed tomography. For display of myocardial tomograms, a technique was devised for superposition of colour coded coronary arteries, showing their relationship to observed perfusion defects. For display of time varying images of the heart, a rapid display routine was developed to enable ventricular wall motion to be evaluated from any angle. Colour display techniques were also applied to this data to produce single images which incorporated kinetic as well as morphological information. The results obtained have confirmed that shaded surface images can be produced using computers currently available in hospital imaging departments. Interactive object modification and real time object display can be achieved without requiring special hardware.

There is a trend in manufacturing towards fully automated production facilities in which all operations are integrated by computer based information systems. The current generation of industrial inspection systems lack the necessary flexibility to operate in these environments. AI based Image Understanding systems have the necessary level of generality, achieved through the use of domain specific object models. These models are used to guide early visual processing, and must be supplied to the system. Current theories in cognitive psychology call for a reevaluation of the role of such 'auxiliary' knowledge in early visual processing. Recent work suggests that very general cognitive processes may build up a hierarchical representation of the world. The emphasis is currently on such generic cognitive processes rather than on the use of world knowledge. A novel approach to image processing, in which emphasis is placed on generic low and intermediate level techniques, is proposed in this thesis. This approach, termed the descriptor approach, delays the use of domain specific models until a full description of the image has been produced. A prototype industrial inspection system has been implemented, based on the descriptor approach: the Hierarchical Scene Description (HISD) system. General image features are extracted from images of populated PCBs, and subsequently transformed into a database of prolog facts by an interface subsystem. Finally the intermediate level vision subsystem uses rules to reason about these features, building up a semantic net based description of the scene. HISD successfully builds up hierarchical descriptions of real industrial PCB images in terms of geometric shapes, their coordinates, and spatial relationships between shapes. The results are displayed graphically and are achieved without the use of any object models, thus avoiding the problems of inflexibility and lack of generality associated with more complex model based systems.

Theory of Artificial Neural Networks (ANNs) could not provide an exact method of weights training. The training is done mostly by iterative trial and error minimisation methods which do not enable the ANNs for time incremental learning. In this thesis, it is shown that the weights successfully produced by an error minimisation method are nothing more than the scaled versions of their respective components of the sample pattern and that the training methods leaves a chance for a neuron to be deceived. An exact method of weight construction is developed in the form of a system of linear equations. A new linear classifier ANN and a number of thresholding procedures are developed. It is proved that the Hopfield network and the Boltzmann machine do not qualify as the reasonable networks. A generalised multiclass linear classifier ANN is developed which is a combination of a newly developed multiclass linear ANN and a newly developed multiclass XOR classifier ANN. A biological neuromuscular system is interpreted as a multiclass linear classifier ANN. A new technique for pattern recognition. especially for images, has been presented with a software check. The technique minimises the design topology of ANNs and enables them to classify a scaled, a mirror image, and a noisy version of the sample pattern. The Continuous Wavelet Transform (CWT), the Discrete Wavelet Transform, and the Wavelet Decomposition has been connected by developing an extend-able and intensifyable system of particular six Gaussian wavelets. A binary transform applicable for every real function is developed. The confusing automatic nature of the CWT is explained along with presenting a new style of defining wavelets. Application of the wavelet transforms for noise reduction and data compression/expansion is explained and their performance is checked through the self developed software. A modification in the CWT is made in order to make their application easier through ANNs. The ANNs are developed and their performance is checked against the self developed software. A new multiresolution zoom-out wavelet transform is developed which expands data without smoothing it. A new wavelet is deduced from the smoothing average filter. Some twodimensional wavelets for noise reduction and data compression/expansion are developed on the same style and their performance is checked through the self developed software. An ANN for CWT using a newly developed two-dimensional wavelet is developed and its activation is explained. Data compression by locating peaks and bottoms of data and setting other elements equals zero is done with the guarantee of reconstruction. The new wavelet transform is modified to reconstruct the data between peaks and bottoms. Peaks and bottoms detecting ANNs are developed and their performance is checked against the self developed software. Procedures for classification are presented with self developed software check. The theory of ANNs requires bit-wise parallel adders and multiplexors. A parallel adder circuit is developed by combining some newly developed basic units for the purpose.

No description available.

No description available.

No description available.

No description available.