published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

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Thesis (Ph.D.)--University of Western Australia, 2006.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

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[Truncated abstract] One common task in Computer Vision is the estimation of three-dimensional surface shape from two-dimensional images. This task is important as a precursor to higher level tasks such as object recognition - since shape of an object gives clues to what the object is - and object modelling for graphics. Many visual cues have been suggested in the literature to provide shape information, including the shading of an object, its occluding contours (the outline of the object that slants away from the viewer) and its appearance from two or more views. If the image exhibits a significant amount of texture, then this too may be used as a shape cue. Here, ‘texture’ is taken to mean the pattern on the surface of the object, such as the dots on a pear, or the tartan pattern on a tablecloth. This problem of estimating the shape of an object based on its texture is referred to as shape-form-texture and it is the subject of this thesis . . . The work in this thesis is likely to impact in a number of ways. The second shape-form-texture algorithm provides one of the most general solutions to the problem. On the other hand, if the assumptions of the first shape-form-texture algorithm are met, this algorithm provides an extremely usable method, in that users should be able to input images of textured objects and click on the frontal texture to quickly reconstruct a fairly good estimation of the surface. And lastly, the algorithm for estimating the transformation between textures can be used as a part of many shape-form-texture algorithms, as well as being useful in other areas of Computer Vision. This thesis gives two examples of other applications for the method: re-texturing an object and placing objects in a scene.

This thesis is concerned with the utilization of object symmety as a cue for segmentation and object recognition. In particular it investigates the problem of detecting 3D bilaterally symmetric objects from affine views. The first part of the thesis investigates the problem of detecting 3D bilateral symmetry within a scene from known point correspondences across two or more affine views. We begin by extending the notion of skewed symmetry to three dimensions, and give a definition in terms of degenerate structure that applies equally to an affine 3D structure or to point correspondences across two or more affine views. We then consider the effects of measurement errors on symmetry detection, and derive an optimal statistical test of degenerate structure, and thereby of 3D-skewed symmetry. We then move on to the problem of searching for 3D skewed symmetric sets within a larger scene. We discuss two approaches to the problem, both of which we have implemented, and we demonstrate fully automatic detection of 3D skewed symmetry on images of uncluttered scenes. We conclude the first part by investing means of verifying the presence of bilateral rather than skewed symmetry in the Euclidean space, by enforcing mutual consistency between multiple skewed symmetric sets, and by drawing on partial knowledge about the camera calibration. The second part of the thesis is concerned with the problem of obtaining feature correspondences across multiple affine views, as required for the detection of symmetry. In particular we investigate the geometric matching constraints that exist between affine views. We start by specilizing the four projective multifocal tensors to the affine case, and use these to carry the bulk of all known projective multi-view matching relations to affine views, unearthing some new relations in the process. Having done that, we address the problem of estimating the affine tensors. We provide a minimal set of constraints on the affine trifocal tensor, and search for ways of estimating the affine tensors from point and line correspondences.

The payoff functions of contingent claims (options) of one variable are prominent in Financial Economics and thus assume a fundamental role in option pricing theory. Some of these payoff functions are continuous, piecewise-defined and linear or affine. Such option payoff functions can be analysed in a useful way when they are represented in additive, Boolean normal, graphical and linear form. The issue of converting such payoff functions expressed in the additive, linear or graphical form into an equivalent Boolean normal form, has been considered by several authors for more than half-a-century to better-understand the role of such functions. One aspect of our study is to unify the foregoing different forms of representation, by creating algorithms that convert a payoff function expressed in graphical form into Boolean normal form and then into the additive form and vice versa. Applications of these algorithms are considered in a general theoretical sense and also in the context of specific option contracts wherever relevant. The use of these algorithms have yielded easy computation of the area enclosed by the graph of various functions using min and max operators in several ways, which, in our opinion, are important in option pricing. To summarise, this study effectively dealt with maximum and minimum operators from several perspectives

Le polytope des m-sous-espaces est défini comme l'enveloppe convexe des vecteurs caractéristiques de tous les sous-espaces de dimension m d'un espace affin fini. Le cas particulier du polytope des hyperplans a été étudié par Maurras (1993) et Anglada et Maurras (2003), qui ont obtenu une description complète des facettes. Le polytope général des m-sous-espaces que nous considérons possède une structure plus complexe, notamment concernant les facettes. Néanmoins, nous établissons dans cette thèse plusieurs familles de facettes. Nous caractérisons également complètement le groupe des automorphismes du polytope ainsi que l'adjacence des sommets du polytope des m-sous-espaces. Un tangle est un ensemble d'hyperplans d'un espace affin contenant un hyperplan par classe d'hyperplans parallèles. Anglada et Maurras ont montré que les tangles définissent des facettes du polytope des hyperplans et que toutes les facettes de ce polytope proviennent de tangles. Nous tentons d'établir une généralisation de ce résultat. Nous élaborons une classification des tangles en familles pour de petites dimensions d'espaces affins. / Doctorat en sciences, Spécialisation mathématiques / info:eu-repo/semantics/nonPublished