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Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 20Ray, Nilanjan (Computing Science)
- 5Zhang, Hong (Computing Science)
- 1Bigras, Gilbert (Laboratory Medicine and Pathology)
- 1Boulanger, Pierre (Computing Science)
- 1Erbilgin, Nadir (Renewable Resources)
- 1Schubert, Matthias (Institute for Informatic, Ludwig-Maximilians-Universität München)
- 1Aziz, Muhammad Usman
- 1Bahri, Fateme
- 1Elkerdawy, Sara
- 1Foroughi, Homa
- 1Guruprasad, Namitha
- 1Hosseini Akbarnejad, Amir Hossein
- 4Computer Vision
- 3Deep Learning
- 3Model Compression
- 2Image Registration
- 2Medical Imaging
- 2Object detection
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Mixed Low-bit Quantization for Model Compression with Layer Importance and Gradient Estimations
DownloadSpring 2022
Deep neural networks (DNNs) have been widely used in the modern world in recent years. However, due to the substantial memory consumption and high computational power use of DNNs, deploying them on devices with limited resources is challenging. Model compression methods can provide us with a...
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Moving Object Detection Using Unsupervised and Weakly Supervised Neural Networks in Videos with Illumination Changes and Dynamic Background
DownloadFall 2023
Background subtraction is a crucial task in computer vision applications, such as video surveillance, traffic monitoring, autonomous navigation, and human-computer interaction. This approach involves acquiring a background model to separate moving objects and the background from an input image....
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Fall 2022
Deep neural networks (DNN) have emerged as the state-of-the-art method in several research areas. DNN is yet to fully permeate resource-constrained computing platforms, such as mobile phones. Accurate DNN models being deeper and wider take considerable memory and time to execute on small devices...
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Spring 2015
There is considerable research work going on segmentation of RGB-D clouds due its applications in tasks like scene understanding, robotics etc. The availability of inexpensive and easy to use RGB-D cameras and computational capabilities of GPUs has lead to development of numerous applications in...
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Fall 2015
Free Viewpoint Video and TV is regarded as the future of digital entertainment allowing users to navigate through multiple video streams of an event to select novel viewpoints. This new capability will be able to give to the users the illusion that they are present at the event. In this thesis,...
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Fall 2011
For the last two decades “active contour” or “snake” has been effective as an interactive image segmentation tool in a wide range of applications, especially for blob-object delineation. In the interactive snake segmentation process, a user draws a rough object outline; next, a cost function is...
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Tiny Object Detection in Remote Sensing Images: End-to-End Super-Resolution and Object Detection with Deep Learning
DownloadFall 2020
In this thesis, we study the problem of detecting small objects on low-resolution (LR) satellite imagery. Small-object detection is a challenging problem, especially from LR images. To tackle the challenge, we propose a method to generate super-resolution images from low-resolution images and...
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Fall 2014
This thesis is concerned with the ultrasonic heart image segmentation problem using parametric active contour model. Most of the existing parametric models consider only either the edge or the regional information. In this thesis, we propose a new parametric active contour model considering both...
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Fall 2021
Ponnamperuma Arachchige, Ayantha Randika
Optical character recognition (OCR) is a widely used pattern recognition application in numerous domains. Several feature-rich commercial OCR solutions and opensource OCR solutions are available for consumers, which can provide moderate to excellent accuracy levels. These solutions are...
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Fall 2023
The recognition performance of Optical Character Recognition (OCR) models can be sub-optimal when document images suffer from various degradations. Supervised learning-based methods for image enhancement can generate high-quality enhanced images. However, these methods require the availability of...