Two basic models of human pattern recognition have been advanced: feature analysis and hypothesis testing. These can only be discriminated by looking at behaviour before recognition. This is studied here by having the subject scan with a pen that writes only where the (invisible) picture is black. Although a computer simulation shows that it is possible and efficient, subjects scanning capital letters and simple shapes with this technique rarely use hypothesis testing.

Thesis (M.Phil.)--Chinese University of Hong Kong. / Bibliography: leaves 122-126.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Repeated Stories is an exploratory project in textile design where the aim is to explore the design of storytelling patterns addressed to children. More precisely, the work examines how patterns can be designed as a tool to encourage curiosity and creativity among children. The work is practice-based, building on concrete experiments with a workshop character, where combinations of textile material, colour, printing techniques and scale are explored. The primary motive for this work is to take advantage of textile design expertise in a social context, to find new areas for competence in making repeats and patterns, and how a social value can be added to patterns. The result is an installation of three hanging textiles, meant for a public space, such as waiting room in a hospital. The work proposes an alternative approach to surface patterns by adding storytelling and give the patterns both a communicative and decorative function.

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A sign pattern (matrix) is a matrix whose entries are from the set {+,–, 0}. A sign pattern matrix A is a spectrally arbitrary pattern if for every monic real polynomial p(x) of degree n there exists a real matrix B whose entries agree in sign with A such that the characteristic polynomial of B is p(x). All 3 × 3 SAP's, as well as tree sign patterns with star graphs that are SAP's, have already been characterized. We investigate tridiagonal sign patterns of order 4. All irreducible tridiagonal SAP's are identified. Necessary and sufficient conditions for an irreducible tridiagonal pattern to be an SAP are found. Some new techniques, such as innovative applications of Gröbner bases for demonstrating that a sign pattern is not potentially nilpotent, are introduced. Some properties of sign patterns that allow every possible inertia are established. Keywords: Sign pattern matrix, Spectrally arbitrary pattern (SAP), Inertially arbitrary pattern (IAP), Tree sign pattern (tsp), Potentially nilpotent pattern, Gröbner basis, Potentially stable pattern, Sign nonsingular, Sign singular

Mes activités de recherche actuelles se situent dans le cadre de la morphogenèse optique et plus généralement de la dynamique non-linéaire. Les systèmes étudiés sont les milieux Kerr (cristaux liquides et fibres optiques) en cavité ou avec feedback optique. J'y étudie plus particulièrement les phénomènes d'instabilités temporelles et spatio-temporelles tels que : - la formation de structures transverses et les instabilités modulationnelles - les solitons dissipatifs et les structures localisées - les systèmes convectifs et leurs instabilités convectives et absolues - les effets du bruit sur ces instabilités, tels que les structures entretenues par le bruit.

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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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