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.
Displaying 1 - 2 of 2
Filtering by
- Creators: Spanias, Andreas
- Creators: Yang, Chenhui
Description
This dissertation includes two parts. First it focuses on discussing robust signal processing algorithms, which lead to consistent performance under perturbation or uncertainty in video target tracking applications. Projective distortion plagues the quality of long sequence mosaicking which results in loosing important target information. Some correction techniques require prior information. A new algorithm is proposed in this dissertation to this very issue. Optimization and parameter tuning of a robust camera motion estimation as well as implementation details are discussed for a real-time application using an ordinary general-purpose computer. Performance evaluations on real-world unmanned air vehicle (UAV) videos demonstrate the robustness of the proposed algorithms. The second half of the dissertation addresses neural signal analysis and modeling. Neural waveforms were recorded from rats' motor cortical areas while rats performed a learning control task. Prior to analyzing and modeling based on the recorded neural signal, neural action potentials are processed to detect neural action potentials which are considered the basic computation unit in the brain. Most algorithms rely on simple thresholding, which can be subjective. This dissertation proposes a new detection algorithm, which is an automatic procedure based on signal-to-noise ratio (SNR) from the neural waveforms. For spike sorting, this dissertation proposes a classification algorithm based on spike features in the frequency domain and adaptive clustering method such as the self-organizing map (SOM). Another major contribution of the dissertation is the study of functional interconnectivity of neurons in an ensemble. These functional correlations among neurons reveal spatial and temporal statistical dependencies, which consequently contributes to the understanding of a neuronal substrate of meaningful behaviors. This dissertation proposes a new generalized yet simple method to study adaptation of neural ensemble activities of a rat's motor cortical areas during its cognitive learning process. Results reveal interesting temporal firing patterns underlying the behavioral learning process.
ContributorsYang, Chenhui (Author) / Si, Jennie (Thesis advisor) / Jassemidis, Leonidas (Committee member) / Buneo, Christopher (Committee member) / Abousleman, Glen (Committee member) / Arizona State University (Publisher)
Created2012
Description
The depth richness of a scene translates into a spatially variable defocus blur in the acquired image. Blurring can mislead computational image understanding; therefore, blur detection can be used for selective image enhancement of blurred regions and the application of image understanding algorithms to sharp regions. This work focuses on blur detection and its application to image enhancement.
This work proposes a spatially-varying defocus blur detection based on the quotient of spectral bands; additionally, to avoid the use of computationally intensive algorithms for the segmentation of foreground and background regions, a global threshold defined using weak textured regions on the input image is proposed. Quantitative results expressed in the precision-recall space as well as qualitative results overperform current state-of-the-art algorithms while keeping the computational requirements at competitive levels.
Imperfections in the curvature of lenses can lead to image radial distortion (IRD). Computer vision applications can be drastically affected by IRD. This work proposes a novel robust radial distortion correction algorithm based on alternate optimization using two cost functions tailored for the estimation of the center of distortion and radial distortion coefficients. Qualitative and quantitative results show the competitiveness of the proposed algorithm.
Blur is one of the causes of visual discomfort in stereopsis. Sharpening applying traditional algorithms can produce an interdifference which causes eyestrain and visual fatigue for the viewer. A sharpness enhancement method for stereo images that incorporates binocular vision cues and depth information is presented. Perceptual evaluation and quantitative results based on the metric of interdifference deviation are reported; results of the proposed algorithm are competitive with state-of-the-art stereo algorithms.
Digital images and videos are produced every day in astonishing amounts. Consequently, the market-driven demand for higher quality content is constantly increasing which leads to the need of image quality assessment (IQA) methods. A training-free, no-reference image sharpness assessment method based on the singular value decomposition of perceptually-weighted normalized-gradients of relevant pixels in the input image is proposed. Results over six subject-rated publicly available databases show competitive performance when compared with state-of-the-art algorithms.
This work proposes a spatially-varying defocus blur detection based on the quotient of spectral bands; additionally, to avoid the use of computationally intensive algorithms for the segmentation of foreground and background regions, a global threshold defined using weak textured regions on the input image is proposed. Quantitative results expressed in the precision-recall space as well as qualitative results overperform current state-of-the-art algorithms while keeping the computational requirements at competitive levels.
Imperfections in the curvature of lenses can lead to image radial distortion (IRD). Computer vision applications can be drastically affected by IRD. This work proposes a novel robust radial distortion correction algorithm based on alternate optimization using two cost functions tailored for the estimation of the center of distortion and radial distortion coefficients. Qualitative and quantitative results show the competitiveness of the proposed algorithm.
Blur is one of the causes of visual discomfort in stereopsis. Sharpening applying traditional algorithms can produce an interdifference which causes eyestrain and visual fatigue for the viewer. A sharpness enhancement method for stereo images that incorporates binocular vision cues and depth information is presented. Perceptual evaluation and quantitative results based on the metric of interdifference deviation are reported; results of the proposed algorithm are competitive with state-of-the-art stereo algorithms.
Digital images and videos are produced every day in astonishing amounts. Consequently, the market-driven demand for higher quality content is constantly increasing which leads to the need of image quality assessment (IQA) methods. A training-free, no-reference image sharpness assessment method based on the singular value decomposition of perceptually-weighted normalized-gradients of relevant pixels in the input image is proposed. Results over six subject-rated publicly available databases show competitive performance when compared with state-of-the-art algorithms.
ContributorsAndrade Rodas, Juan Manuel (Author) / Spanias, Andreas (Thesis advisor) / Turaga, Pavan (Thesis advisor) / Abousleman, Glen (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2019