Dissertation/Thesis Abstract

A Novel Computer Vision-Based Framework for Supervised Classification of Energy Outbreak Phenomena
by Abusaleh, Sumaya, Ph.D., University of Bridgeport, 2016, 128; 10746723
Abstract (Summary)

Today, there is a need to implement a proper design of an adequate surveillance system that detects and categorizes explosion phenomena in order to identify the explosion risk to reduce its impact through mitigation and preparedness. This dissertation introduces state-of-the-art classification of explosion phenomena through pattern recognition techniques on color images. Consequently, we present a novel taxonomy for explosion phenomena. In particular, we demonstrate different aspects of volcanic eruptions and nuclear explosions of the proposed taxonomy that include scientific formation, real examples, existing monitoring methodologies, and their limitations. In addition, we propose a novel framework designed to categorize explosion phenomena against non-explosion phenomena. Moreover, a new dataset, Volcanic and Nuclear Explosions (VNEX), was collected. The totality of VNEX is 10, 654 samples, and it includes the following patterns: pyroclastic density currents, lava fountains, lava and tephra fallout, nuclear explosions, wildfires, fireworks, and sky clouds.

In order to achieve high reliability in the proposed explosion classification framework, we propose to employ various feature extraction approaches. Thus, we calculated the intensity levels to extract the texture features. Moreover, we utilize the YCbCr color model to calculate the amplitude features. We also employ the Radix-2 Fast Fourier Transform to compute the frequency features. Furthermore, we use the uniform local binary patterns technique to compute the histogram features. Additionally, these discriminative features were combined into a single input vector that provides valuable insight of the images, and then fed into the following classification techniques: Euclidian distance, correlation, k-nearest neighbors, one-against-one multiclass support vector machines with different kernels, and the multilayer perceptron model. Evaluation results show the design of the proposed framework is effective and robust. Furthermore, a trade-off between the computation time and the classification rate was achieved.

Indexing (document details)
Advisor: Mahmood, Ausif, Elleithy, Khaled M.
Commitee: Faezipour, Miad, Patel, Sarosh, Rizvi, Syed
School: University of Bridgeport
Department: Computer Science and Engineering
School Location: United States -- Connecticut
Source: DAI-B 79/07(E), Dissertation Abstracts International
Subjects: Computer Engineering, Artificial intelligence, Computer science
Keywords: Computer vision, Image classification, Nuclear explosions, Supervised learning, Vnex dataset, Volcanoes
Publication Number: 10746723
ISBN: 9780355677850
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