This research focuses on iris recognition, the most accurate form of biometric identification. The robustness of iris recognition comes from the unique characteristics of the human, and the permanency of the iris texture as it is stable over human life, and the environmental effects cannot easily alter its shape. In most iris recognition systems, ideal image acquisition conditions are assumed. These conditions include a near infrared (NIR) light source to reveal the clear iris texture as well as look and stare constraints and close distance from the capturing device. However, the recognition accuracy of the-state-of-the-art systems decreases significantly when these constraints are relaxed. Recent advances have proposed different methods to process iris images captured in unconstrained environments. While these methods improve the accuracy of the original iris recognition system, they still have segmentation and feature selection problems, which results in high FRR (False Rejection Rate) and FAR (False Acceptance Rate) or in recognition failure. In the first part of this thesis, a novel segmentation algorithm for detecting the limbus and pupillary boundaries of human iris images with a quality assessment process is proposed. The algorithm first searches over the HSV colour space to detect the local maxima sclera region as it is the most easily distinguishable part of the human eye. The parameters from this stage are then used for eye area detection, upper/lower eyelid isolation and for rotation angle correction. The second step is the iris image quality assessment process, as the iris images captured under unconstrained conditions have heterogeneous characteristics. In addition, the probability of getting a mis-segmented sclera portion around the outer ring of the iris is very high, especially in the presence of reflection caused by a visible wavelength light source. Therefore, quality assessment procedures are applied for the classification of images from the first step into seven different categories based on the average of their RGB colour intensity. An appropriate filter is applied based on the detected quality. In the third step, a binarization process is applied to the detected eye portion from the first step for detecting the iris outer ring based on a threshold value defined on the basis of image quality from the second step. Finally, for the pupil area segmentation, the method searches over the HSV colour space for local minima pixels, as the pupil contains the darkest pixels in the human eye. In the second part, a novel discriminating feature extraction and selection based on the Curvelet transform are introduced. Most of the state-of-the-art iris recognition systems use the textural features extracted from the iris images. While these fine tiny features are very robust when extracted from high resolution clear images captured at very close distances, they show major weaknesses when extracted from degraded images captured over long distances. The use of the Curvelet transform to extract 2D geometrical features (curves and edges) from the degraded iris images addresses the weakness of 1D texture features extracted by the classical methods based on textural analysis wavelet transform. Our experiments show significant improvements in the segmentation and recognition accuracy when compared to the-state-of-the-art results.

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Humans are well suited to the reading of textual information, but unfortunately it has not yet been possible to develop a machine to emulate this form of human behaviour. In the past, machines have been characterised by having static forms of specific knowledge necessary for character recognition. The resulting form of reading behaviour is most uncharacteristic of the way humans perceive textual information. The major problem with handprinted character recognition is the infinite variability in the character shapes and the ambiguities many of these shapes exhibit. Human perception of handprinted characters makes extensive use of "world knowledge" to remove such ambiguities. Humans are also continually modifying their world knowledge to further enhance their reading behaviour by acquiring new knowledge as they read. An information processing model for perception and learning of handprinted characters is proposed. The function of the model is to enable ambiguous character descriptions to converge to single character classifications. The accuracy of this convergence improves with reading experience on handprinted text. The model consists of three compon,ent parts. Firstly, a character classifier to recognise character patterns. These patterns may be both distorted anq noisy, where distortion is defined to be a consistent variability from known archetypical character descriptions and noise as a random inconsistent variability in character shape. Secondly, a perceptive mechanism that makes inferences from an incomplete linguistic world model of an author or of a specific domain of discourse from many authors. Finally, a incremental learning capability is integrated into the character classifier and perceptive mechanisms. This is to enable the internal world model to be continually adaptive to either changes in the domain of discourse or to different authors. A demonstrator is described, together with a summary of experimental results that clearly show the improvement in machine perception which results from continuous incremental learning.

This thesis presents a new and powerful approach to the development of a general purpose machine vision system. The approach is inspired from anatomical considerations of the primate's vision system. The geometrical arrangement of cones on a primate's retina can be described in terms of a hexagonal grid. The importance of the hexagonal grid is that it possesses special computational features that are pertinent to the vision process. The fundamental thrust of this thesis emanates from the observation that this hexagonal grid can be described in terms of the mathematical object known as a Euclidean ring. The Euclidean ring is employed to generate an algebra of linear transformations which are appropriate for the processing of multidimensional vision data. A parallel autonomous segmentation algorithm for multidimensional vision data is described. The algebra and segmentation algorithm are implemented on a network of transputers. The implementation is discussed in the context of the outline of a general purpose machine vision system's design.

This masters thesis describes the development of signal processing and patternrecognition in monitoring Parkison’s disease. It involves the development of a signalprocess algorithm and passing it into a pattern recogniton algorithm also. Thesealgorithms are used to determine , predict and make a conclusion on the study ofparkison’s disease. We get to understand the nature of how the parkinson’s disease isin humans.