Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xv, 105 p. : ill. (some col.). Advisor: Jogikal Jagadeesh, Department of Electrical and Computer Engineering. Includes bibliographical references (p. 101-105).

Thesis (M.Sc.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on Apr. 30, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta. Includes bibliographical references.

Thesis (Ph.D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 123 pages. Includes vita. Includes bibliographical references.

Thesis (Ph.D)--Stanford University, 1999. / Title from pdf t.p. (viewed April 3, 2002). "January 1999." "Adminitrivia V1/Prg/20000907"--Metadata.

Thesis (Ph.D.)--University of Western Australia, 2004.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--Brandeis University, 2010. / "UMI:3390484." MICROFILM COPY ALSO AVAILABLE IN THE UNIVERSITY ARCHIVES. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2005. / Typescript. Includes bibliographical references (p. 91-93).

In this research work, we develop an obstacle detection and emergency exit sign recognition system on a mobile phone by extending the feature from accelerated segment test detector with Harris corner filter. The first step often required for many vision based applications is the detection of objects of interest in an image. Hence, in this research work, we introduce emergency exit sign detection method using colour histogram. The hue and saturation component of an HSV colour model are processed into features to build a 2D colour histogram. We backproject a 2D colour histogram to detect emergency exit sign from a captured image as the first task required before performing emergency exit sign recognition. The result of classification shows that the 2D histogram is fast and can discriminate between objects and background with accuracy. One of the challenges confronting object recognition methods is the type of image feature to compute. In this work therefore, we present two feature detectors and descriptor methods based on the feature from accelerated segment test detector with Harris corner filter. The first method is called Upright FAST-Harris and binary detector (U-FaHB), while the second method Scale Interpolated FAST-Harris and Binary (SIFaHB). In both methods, feature points are extracted using the accelerated segment test detectors and Harris filter to return the strongest corner points as features. However, in the case of SIFaHB, the extraction of feature points is done across the image plane and along the scale-space. The modular design of these detectors allows for the integration of descriptors of any kind. Therefore, we combine these detectors with binary test descriptor like BRIEF to compute feature regions. These detectors and the combined descriptor are evaluated using different images observed under various geometric and photometric transformations and the performance is compared with other detectors and descriptors. The results obtained show that our proposed feature detector and descriptor method is fast and performs better compared with other methods like SIFT, SURF, ORB, BRISK, CenSurE. Based on the potential of U-FaHB detector and descriptor, we extended it for use in optical flow computation, which we termed the Nearest-flow method. This method has the potential of computing flow vectors for use in obstacle detection. Just like any other new methods, we evaluated the Nearest flow method using real and synthetic image sequences. We compare the performance of the Nearest-flow with other methods like the Lucas and Kanade, Farneback and SIFT-flow. The results obtained show that our Nearest-flow method is faster to compute and performs better on real scene images compared with the other methods. In the final part of this research, we demonstrate the application potential of our proposed methods by developing an obstacle detection and exit sign recognition system on a camera phone and the result obtained shows that the methods have the potential to solve this vision based object detection and recognition problem.

Title: The subnuclear localisation of Notch responsive genes. Candidate Name: Matthew Jones Notch signalling is a highly conserved cell-cell communication pathway with critical roles in metazoan development and mutations in Notch pathway components are implicated in many types of cancer. Notch is an excellent and well-studied model of biological signalling and gene regulation, with a single intracellular messenger, one receptor and two ligands in Drosophila. However, despite the limited number of chemical players involved, a striking number of different outcomes arise. Molecular studies have shown that Notch activates different targets in different cell types and it is well known that Notch is important for maintaining a stem cell fate in some situations and driving differentiation in others. Thus some of the factors affecting the regulation of Notch target genes are yet to be discovered. Previous studies in various organisms have found that the location of a gene within the nucleus is important for its regulation and genome reorganisation can occur following gene activation or during development. Therefore this project aimed to label individual Notch responsive loci and determine their subnuclear localisation. In order to tag loci of interest a CRISPR/Cas9 genome-editing method was established that enabled the insertion of locus tags at Notch targets, namely the well-characterized Enhancer of split locus and also dpn and Hey, two transcription factors involved in neural cell fate decisions. The ParB/Int system is a recently developed locus tagging system and is not well characterised in Drosophila. It has a number of advantages over the traditional LacO/LacI-GFP locus tagging system as it does not rely on binding site repeats for signal amplification and can label two loci simultaneously in different colours. This thesis characterised the ParB/Int system in the Drosophila salivary gland and larval L3 neuroblast. Using 3D image segmentation hundreds of nuclei were reconstructed and a volume based normalisation method was applied to determine the subnuclear localisation of several Notch targets with and without genetic manipulations of the Notch pathway.