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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Subject
Date Range
2011 2018


The origin and function of color in animals has been a subject of great interest for taxonomists and ecologists in recent years. Coloration in animals is useful for many important functions like species identification, camouflage and understanding evolutionary relationships. Quantitative measurements of color signal and patch size in mammals, birds and reptiles, to name a few are strong indicators of sexual selection cues and individual health. These measurements provide valuable insights into the impact of environmental conditions on habitat and breeding of mammals, birds and reptiles. Recent advances in the area of digital cameras and sensors have led to a ...

Contributors
Borkar, Tejas Shyam, Karam, Lina J, Li, Baoxin, et al.
Created Date
2013

Diabetic retinopathy (DR) is a common cause of blindness occurring due to prolonged presence of diabetes. The risk of developing DR or having the disease progress is increasing over time. Despite advances in diabetes care over the years, DR remains a vision-threatening complication and one of the leading causes of blindness among American adults. Recent studies have shown that diagnosis based on digital retinal imaging has potential benefits over traditional face-to-face evaluation. Yet there is a dearth of computer-based systems that can match the level of performance achieved by ophthalmologists. This thesis takes a fresh perspective in developing a computer-based ...

Contributors
Chandakkar, Parag Shridhar, Li, Baoxin, Turaga, Pavan, et al.
Created Date
2012

Light field imaging is limited in its computational processing demands of high sampling for both spatial and angular dimensions. Single-shot light field cameras sacrifice spatial resolution to sample angular viewpoints, typically by multiplexing incoming rays onto a 2D sensor array. While this resolution can be recovered using compressive sensing, these iterative solutions are slow in processing a light field. We present a deep learning approach using a new, two branch network architecture, consisting jointly of an autoencoder and a 4D CNN, to recover a high resolution 4D light field from a single coded 2D image. This network decreases reconstruction time ...

Contributors
Gupta, Mayank, Turaga, Pavan, Yang, Yezhou, et al.
Created Date
2017

In UAVs and parking lots, it is typical to first collect an enormous number of pixels using conventional imagers. This is followed by employment of expensive methods to compress by throwing away redundant data. Subsequently, the compressed data is transmitted to a ground station. The past decade has seen the emergence of novel imagers called spatial-multiplexing cameras, which offer compression at the sensing level itself by providing an arbitrary linear measurements of the scene instead of pixel-based sampling. In this dissertation, I discuss various approaches for effective information extraction from spatial-multiplexing measurements and present the trade-offs between reliability of the ...

Contributors
Kulkarni, Kuldeep Sharad, Turaga, Pavan, Li, Baoxin, et al.
Created Date
2017

Deep learning architectures have been widely explored in computer vision and have depicted commendable performance in a variety of applications. A fundamental challenge in training deep networks is the requirement of large amounts of labeled training data. While gathering large quantities of unlabeled data is cheap and easy, annotating the data is an expensive process in terms of time, labor and human expertise. Thus, developing algorithms that minimize the human effort in training deep models is of immense practical importance. Active learning algorithms automatically identify salient and exemplar samples from large amounts of unlabeled data and can augment maximal information ...

Contributors
Ranganathan, Hiranmayi, Sethuraman, Panchanathan, Papandreou-Suppappola, Antonia, et al.
Created Date
2018

Video denoising has been an important task in many multimedia and computer vision applications. Recent developments in the matrix completion theory and emergence of new numerical methods which can efficiently solve the matrix completion problem have paved the way for exploration of new techniques for some classical image processing tasks. Recent literature shows that many computer vision and image processing problems can be solved by using the matrix completion theory. This thesis explores the application of matrix completion in video denoising. A state-of-the-art video denoising algorithm in which the denoising task is modeled as a matrix completion problem is chosen ...

Contributors
Maguluri, Hima Bindu, Li, Baoxin, Turaga, Pavan, et al.
Created Date
2013

Today's world is seeing a rapid technological advancement in various fields, having access to faster computers and better sensing devices. With such advancements, the task of recognizing human activities has been acknowledged as an important problem, with a wide range of applications such as surveillance, health monitoring and animation. Traditional approaches to dynamical modeling have included linear and nonlinear methods with their respective drawbacks. An alternative idea I propose is the use of descriptors of the shape of the dynamical attractor as a feature representation for quantification of nature of dynamics. The framework has two main advantages over traditional approaches: ...

Contributors
VENKATARAMAN, VINAY, Turaga, Pavan, Papandreou-Suppappol, Antonia, et al.
Created Date
2016

In this thesis, we consider the problem of fast and efficient indexing techniques for time sequences which evolve on manifold-valued spaces. Using manifolds is a convenient way to work with complex features that often do not live in Euclidean spaces. However, computing standard notions of geodesic distance, mean etc. can get very involved due to the underlying non-linearity associated with the space. As a result a complex task such as manifold sequence matching would require very large number of computations making it hard to use in practice. We believe that one can device smart approximation algorithms for several classes of ...

Contributors
Anirudh, Rushil, Turaga, Pavan, Spanias, Andreas, et al.
Created Date
2012

Monitoring vital physiological signals, such as heart rate, blood pressure and breathing pattern, are basic requirements in the diagnosis and management of various diseases. Traditionally, these signals are measured only in hospital and clinical settings. An important recent trend is the development of portable devices for tracking these physiological signals non-invasively by using optical methods. These portable devices, when combined with cell phones, tablets or other mobile devices, provide a new opportunity for everyone to monitor one’s vital signs out of clinic. This thesis work develops camera-based systems and algorithms to monitor several physiological waveforms and parameters, without having to ...

Contributors
Shao, Dangdang, Tao, Nongjian, Li, Baoxin, et al.
Created Date
2016

The quality of real-world visual content is typically impaired by many factors including image noise and blur. Detecting and analyzing these impairments are important steps for multiple computer vision tasks. This work focuses on perceptual-based locally adaptive noise and blur detection and their application to image restoration. In the context of noise detection, this work proposes perceptual-based full-reference and no-reference objective image quality metrics by integrating perceptually weighted local noise into a probability summation model. Results are reported on both the LIVE and TID2008 databases. The proposed metrics achieve consistently a good performance across noise types and across databases as ...

Contributors
Zhu, Tong, Karam, Lina, Li, Baoxin, et al.
Created Date
2016