Pattern detection is a well-studied area of computer vision, but still current methods are unstable in images of poor quality. This thesis describes improvements over contemporary methods in the fast detection of unseen patterns in a large corpus of videos that vary tremendously in colour and texture definition, captured "in the wild" by mobile devices and surveillance cameras. We focus on three key areas of this broad subject; First, we identify consistency weaknesses in existing techniques of processing an image and it's horizontally reflected (mirror) image. This is important in police investigations where subjects change their appearance to try to avoid recognition, and we propose that invariance to horizontal reflection should be more widely considered in image description and recognition tasks too. We observe online Deep Learning system behaviours in this respect, and provide a comprehensive assessment of 10 popular low level feature detectors. Second, we develop simple and fast algorithms that combine to provide memory- and processing-efficient feature matching. These involve static scene elimination in the presence of noise and on-screen time indicators, a blur-sensitive feature detection that finds a greater number of corresponding features in images of varying sharpness, and a combinatorial texture and colour feature matching algorithm that matches features when either attribute may be poorly defined. A comprehensive evaluation is given, showing some improvements over existing feature correspondence methods. Finally, we study random decision forests for pattern detection. A new method of indexing patterns in video sequences is devised and evaluated. We automatically label positive and negative image training data, reducing a task of unsupervised learning to one of supervised learning, and devise a node split function that is invariant to mirror reflection and rotation through 90 degree angles. A high dimensional vote accumulator encodes the hypothesis support, yielding implicit back-projection for pattern detection.

The objects discovered in Tomb 79 at the necropolis of Salamis, Cyprus have garnered much attention since their discovery. The material from this tomb, however, needs an in-depth, object-by-object analysis that will lead to a greater understanding of the burial as a whole. In my thesis, I offer a detailed case study of a single item, an ivory-covered wooden chair—so-called Throne Γ—as exemplifying an approach to this analysis. Based on the excavation team’s exacting reconstruction, the chair is four-legged with armrests and a slightly curved backrest. Ivory overlays the entirety of the chair except on a few sections of the backrest where the wood shows through. Here as well, both figural and geometric designs decorate the ivory, and the top bar was originally overlaid with gold. As a whole, Throne Γ would have appeared as a solid ivory object, embellished with wood and gold, and was likely draped with textiles. In this study, I analyze Throne Γ as an agent of identity. To do so, I follow the example of other scholars such as Irene Winter and Marian Feldman and employ the theory of object agency, addressing Throne Γ as an affective entity. When placed in a social context—i.e., when involved in human interaction—such agentive objects actively influence their surroundings. In this case, I analyze how Throne Γ affected the individual in whose tomb it was buried. I argue that through its various affective “mechanisms”—its nature as a luxury object, the value of its ivory material, its sensory qualities (including luminosity, texture, and fragrance), its iconography, and its ritual function—Throne Γ projected a king-like identity upon the deceased individual from Tomb 79. His actual political and social power during his lifetime, however, may have been less than that suggested by the mechanisms of the chair. The inclusion of Throne Γ in the burial was therefore a conscious choice and the identity the chair projected deliberate. It was meant to agentively mark, and thus legitimize, the deceased as a politically-able, diplomatically-savvy, and divinely-touched figure in the early days of monarchy on Cyprus. / text

Doctor of Philosophy / This thesis is based on research on financial time series analysis using pattern recognition methods. The first part of this research focuses on univariate time series analysis using different pattern recognition methods. First, probabilities of basic patterns are used to represent the features of a section of time series. This feature can remove noise from the time series by statistical probability. It is experimentally proven that this feature is successful for pattern repeated time series. Second, a multiscale Gaussian gravity as a pattern relationship measurement which can describe the direction of the pattern relationship is introduced to pattern clustering. By searching for the Gaussian-gravity-guided nearest neighbour of each pattern, this clustering method can easily determine the boundaries of the clusters. Third, a method that unsupervised pattern classification can be transformed into multiscale supervised pattern classification by multiscale supervisory time series or multiscale filtered time series is presented. The second part of this research focuses on multivariate time series analysis using pattern recognition. A systematic method is proposed to find the independent variables of a group of share prices by time series clustering, principal component analysis, independent component analysis, and object recognition. The number of dependent variables is reduced and the multivariate time series analysis is simplified by time series clustering and principal component analysis. Independent component analysis aims to find the ideal independent variables of the group of shares. Object recognition is expected to recognize those independent variables which are similar to the independent components. This method provides a new clue to understanding the stock market and to modelling a large time series database.

Doctor of Philosophy / This thesis is based on research on financial time series analysis using pattern recognition methods. The first part of this research focuses on univariate time series analysis using different pattern recognition methods. First, probabilities of basic patterns are used to represent the features of a section of time series. This feature can remove noise from the time series by statistical probability. It is experimentally proven that this feature is successful for pattern repeated time series. Second, a multiscale Gaussian gravity as a pattern relationship measurement which can describe the direction of the pattern relationship is introduced to pattern clustering. By searching for the Gaussian-gravity-guided nearest neighbour of each pattern, this clustering method can easily determine the boundaries of the clusters. Third, a method that unsupervised pattern classification can be transformed into multiscale supervised pattern classification by multiscale supervisory time series or multiscale filtered time series is presented. The second part of this research focuses on multivariate time series analysis using pattern recognition. A systematic method is proposed to find the independent variables of a group of share prices by time series clustering, principal component analysis, independent component analysis, and object recognition. The number of dependent variables is reduced and the multivariate time series analysis is simplified by time series clustering and principal component analysis. Independent component analysis aims to find the ideal independent variables of the group of shares. Object recognition is expected to recognize those independent variables which are similar to the independent components. This method provides a new clue to understanding the stock market and to modelling a large time series database.