Engineering Commons^™

Atmospheric Turbulence Distortion In Video: Restoration Utilizing Sparse Analysis, Benjamin J. Sanda

Dissertations

The removal of atmospheric turbulence (AT) distortion in long range imaging is one of the most challenging areas of research in imaging processing with an immediate need for solutions in several applications such as in military and transportation systems. AT exacerbates distortion due to non-linear geometric blur and scintillations in long-distance images and videos, severely reducing image quality and information interpretation. AT negatively impacts both human and computer vision systems, compromising visibility essential for accurate object identification and tracking.

In this dissertation, a novel sparse analysis framework is developed to address efficient AT blur and scintillation removal in video. Operating …

Application Of Artificial Intelligence And Geographic Information System For Developing Automated Walkability Score, Md Mehedi Hasan

Dissertations

Walking is considered as one of the major modes of active transportation, which contributes to the livability of cities. It is highly important to ensure walk friendly sidewalks to promote human physical activities along roads. Over the last two decades, different walk scores were estimated in respect to walkability measures by applying different methods and approaches. However, in the era of big data and machine learning revolution, there is still a gap to measure the composite walkability score in an automated way by applying and quantifying the activityfriendliness of walkable streets. In this study, a street-level automated walkability score was …

Deep Learning For Remote Sensing Image Processing, Yan Lu

Computational Modeling & Simulation Engineering Theses & Dissertations

Remote sensing images have many applications such as ground object detection, environmental change monitoring, urban growth monitoring and natural disaster damage assessment. As of 2019, there were roughly 700 satellites listing “earth observation” as their primary application. Both spatial and temporal resolutions of satellite images have improved consistently in recent years and provided opportunities in resolving fine details on the Earth's surface. In the past decade, deep learning techniques have revolutionized many applications in the field of computer vision but have not fully been explored in remote sensing image processing. In this dissertation, several state-of-the-art deep learning models have been …

Applications Of Image Processing Techniques And Spatial Data Analytics For Pressure Mapping Analysis, Joan Yamil Martinez

Dissertations

The technological advancements in sensors, monitoring systems, and tracking devices are changing how we study our environment; big data sets are becoming more and more prevalent due to the increase of information gathered with ease. One system benefiting from these technological improvements is pressure mapping technology, an easy-to-use and cost-effective solution for assessing contact pressure distributions.

Pressure mapping systems generally produce data sets of very large volume, especially when used for continuous tracking and monitoring, and are widely used for research in fields of ergonomics, sports, industries, and health disciplines. Pressure mapping systems are particularly important in the study of …

Experimental And Computational Tools For Single Cell Analysis In Cancer Diagnostics, Manibarathi Vaithiyanathan

LSU Doctoral Dissertations

Substantial evidence shows that cellular heterogeneity commonly exists within an isogenic or clonal population. Whether in isolation or caused through a combination of the above events, cellular heterogeneity can dramatically influence cellular decision making and cell fate, however, this can be masked by the average response from a population. One approach to solve this issue is to analyze a population at the individual cell level. The goal of this work is to develop high-throughput experimental and computational platforms to screen and quantify single cancer cells for specific intracellular enzyme activities. An interdisciplinary approach was taken to 1) better understand the …

Automatic Shoreline Digitization And Mesh Element Sizing For Hydrodynamic Modeling, Henok Kefelegn

LSU Doctoral Dissertations

The first and most critical step in any coastal hydrodynamics and transport process modeling is identifying land-water boundaries. In a coastal wetland, this has always been a challenge due to the complexity of the wetland and lack of efficient methods, calling for efficient and effective methods to extract and digitize the shorelines. While coastline feature extraction has been increasingly researched, its application in hydrodynamic and environmental modeling, without morphological adjustment, remains limited and suboptimal. Further, there has been a paucity of cost-effective, contextually adaptive and high-quality methods to generate meshes, especially for coastal hydrodynamic modeling. This study has developed and …

Video And Image Super-Resolution Via Deep Learning With Attention Mechanism, Xuan Xu

Graduate Theses, Dissertations, and Problem Reports

Image demosaicing, image super-resolution and video super-resolution are three important tasks in color imaging pipeline. Demosaicing deals with the recovery of missing color information and generation of full-resolution color images from so-called Color filter Array (CFA) such as Bayer pattern. Image super-resolution aims at increasing the spatial resolution and enhance important structures (e.g., edges and textures) in super-resolved images. Both spatial and temporal dependency are important to the task of video super-resolution, which has received increasingly more attention in recent years. Traditional solutions to these three low-level vision tasks lack generalization capability especially for real-world data. Recently, deep learning methods …