Physical Sciences and Mathematics Commons^™

Continuum Modeling Of Active Nematics Via Data-Driven Equation Discovery, Connor Robertson

Dissertations

Data-driven modeling seeks to extract a parsimonious model for a physical system directly from measurement data. One of the most interpretable of these methods is Sparse Identification of Nonlinear Dynamics (SINDy), which selects a relatively sparse linear combination of model terms from a large set of (possibly nonlinear) candidates via optimization. This technique has shown promise for synthetic data generated by numerical simulations but the application of the techniques to real data is less developed. This dissertation applies SINDy to video data from a bio-inspired system of mictrotubule-motor protein assemblies, an example of nonequilibrium dynamics that has posed a significant …

Advances In Deep Learning With Applications To Computer Vision And Astronomy, Zhihang Hu

Dissertations

Deep Learning has spanned a variety of applications in computer vision as well as computational astronomy. These two aspects obtained similar data structure, therefore, their solutions can be transferable between each other. This dissertation look into two video-related tasks in computer vision and propose a novel problem in computational astronomy.

Specifically, acquiring an in-depth understanding of videos has been a cornerstone problem in computer vision. This problem has been studied by various researchers from different perspectives, among which video prediction has attracted much attention. Video prediction aims to generate the pixels of future frames given a sequence of context frames. …

Annotation Of Multimedia Learning Materials For Semantic Search, Sheetal Rajgure

Dissertations

Multimedia is the main source for online learning materials, such as videos, slides and textbooks, and its size is growing with the popularity of online programs offered by Universities and Massive Open Online Courses (MOOCs). The increasing amount of multimedia learning resources available online makes it very challenging to browse through the materials or find where a specific concept of interest is covered. To enable semantic search on the lecture materials, their content must be annotated and indexed. Manual annotation of learning materials such as videos is tedious and cannot be envisioned for the growing quantity of online materials. One …

Novel Color And Local Image Descriptors For Content-Based Image Search, Sugata Banerji

Dissertations

Content-based image classification, search and retrieval is a rapidly-expanding research area. With the advent of inexpensive digital cameras, cheap data storage, fast computing speeds and ever-increasing data transfer rates, millions of images are stored and shared over the Internet every day. This necessitates the development of systems that can classify these images into various categories without human intervention and on being presented a query image, can identify its contents in order to retrieve similar images.

Towards that end, this dissertation focuses on investigating novel image descriptors based on texture, shape, color, and local information for advancing content-based image search. Specifically, …

Face Recognition Using Multiple Features In Different Color Spaces, Zhiming Liu

Dissertations

Face recognition as a particular problem of pattern recognition has been attracting substantial attention from researchers in computer vision, pattern recognition, and machine learning. The recent Face Recognition Grand Challenge (FRGC) program reveals that uncontrolled illumination conditions pose grand challenges to face recognition performance. Most of the existing face recognition methods use gray-scale face images, which have been shown insufficient to tackle these challenges. To overcome this challenging problem in face recognition, this dissertation applies multiple features derived from the color images instead of the intensity images only.

First, this dissertation presents two face recognition methods, which operate in different …

Solar Activity Detection And Prediction Using Image Processing And Machine Learning Techniques, Gang Fu

Dissertations

The objective of the research in this dissertation is to develop the methods for automatic detection and prediction of solar activities, including prominence eruptions, emerging flux regions and solar flares. Image processing and machine learning techniques are applied in this study. These methods can be used for automatic observation of solar activities and prediction of space weather that may have great influence on the near earth environment.

The research presented in this dissertation covers the following topics: i) automatic detection of prominence eruptions (PBs), ii) automatic detection of emerging flux regions (EFRs), and iii) automatic prediction of solar flares.

In …

Image Segmentation And Pattern Classification Using Support Vector Machines, Shouxian Cheng

Dissertations

Image segmentation and pattern classification have long been important topics in computer science research. Image segmentation is one of the basic and challenging lower-level image processing tasks. Feature extraction, feature reduction, and classifier design based on selected features are the three essential issues for the pattern classification problem.

In this dissertation, an automatic Seeded Region Growing (SRG) algorithm for color image segmentation is developed. In the SRG algorithm, the initial seeds are automatically determined. An adaptive morphological edge-linking algorithm to fill in the gaps between edge segments is designed. Broken edges are extended along their slope directions by using the …

Automatic Solar Feature Detection Using Image Processing And Pattern Recognition Techniques, Ming Qu

Dissertations

The objective of the research in this dissertation is to develop a software system to automatically detect and characterize solar flares, filaments and Corona Mass Ejections (CMEs), the core of so-called solar activity. These tools will assist us to predict space weather caused by violent solar activity. Image processing and pattern recognition techniques are applied to this system.

For automatic flare detection, the advanced pattern recognition techniques such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM) are used. By tracking the entire process of flares, the motion properties of two-ribbon flares are derived automatically. In …

Image Morphological Processing, Vijayalakshmi Gaddipati

Dissertations

Mathematical Morphology with applications in image processing and analysis has been becoming increasingly important in today's technology. Mathematical Morphological operations, which are based on set theory, can extract object features by suitably shaped structuring elements. Mathematical Morphological filters are combinations of morphological operations that transform an image into a quantitative description of its geometrical structure based on structuring elements. Important applications of morphological operations are shape description, shape recognition, nonlinear filtering, industrial parts inspection, and medical image processing.

In this dissertation, basic morphological operations, properties and fuzzy morphology are reviewed. Existing techniques for solving corner and edge detection are presented. …

Interpreting And Integrating Landsat Remote Sensing Image And Geographic Information System By Fuzzy Unsupervised Clustering Algorithm, Gwotsong Philip Chen

Theses

Due to the resolution of Landsat images and the multiplicity of the terrain, it is improper to assign each pixel in an image to one of a number of land cover types by using the conventional remote sensing classification method. This is also known as the hard partition method. The concept of the fuzzy set provides the means to resolve this problem. This paper presents a two-pass-mode fuzzy unsupervised clustering algorithm.

In the first passing, the cluster mean vectors which represent the geographic attributes or the land cover types are derived. In the second passing, the concept of fuzzy set …

A Heuristical Method Of Corner Points Detection On An Image Boundary, Qiulin Li

Theses

A heuristics-based method of corner points detection on an image boundary is proposed and implemented. The method uses the sampled boundary distances to find all of the candidate corner points along the image boundary. Then the curvature characteristical value is used to measure the severity of curvature change of the candidate points. Those candidates whose curvature characteristical value is under some threshold are eliminated. The paper also proposed some mechanism to reduce the effect of noises on the boundary. Experiments show that it is an efficient method and it gives satisfactory results on some image boundaries.

The Performance Of Training Pattern Sets In A New Art-Based Neural Architecture For Image Enhancement, Fu-Chun Chang

Theses

Neural network can be applied on the image enhancement after adding another two layers into the Adaptive Resonance Theory architectures (ART 1). The analysis for selecting a nice training pattern set associate the appropriate vigilance values is the main concerns in this thesis. For a single training pattern ,the network can act as a mathematical morphology operators such as erosion , dilation, opening and closing. With more than one training patterns in the network, 16 experiments are tested and are compared to each other in order to find the best selection for doing the image enhancement work. With both the …