Date of Award
Spring 5-2018
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Dr. Md Tamjidul Hoque
Second Advisor
Dr. Mahdi Abdelguerfi
Third Advisor
Dr. Elias Ioup
Fourth Advisor
Dr. Christopher Michael
Abstract
Rip current images are useful for assisting in climate studies but time consuming to manually annotate by hand over thousands of images. Object detection is a possible solution for automatic annotation because of its success and popularity in identifying regions of interest in images, such as human faces. Similarly to faces, rip currents have distinct features that set them apart from other areas of an image, such as more generic patterns of the surf zone. There are many distinct methods of object detection applied in face detection research. In this thesis, the best fit for a rip current object detector is found by comparing these methods. In addition, the methods are improved with Haar features exclusively created for rip current images. The compared methods include max distance from the average, support vector machines, convolutional neural networks, the Viola-Jones object detector, and a meta-learner. The presented results are compared for accuracy, false positive rate, and detection rate. Viola-Jones has the top base-line performance by achieving a detection rate of 0.88 and identifying only 15 false positives in the test image set of 53 rip currents. The described meta-learner integrates the presented Haar features, which are developed in accordance with the original Viola-Jones algorithm. Ada-Boost, a feature ranking algorithm, shows that the newly presented Haar features extract more meaningful data from rip current images than some of the current features. The meta-classifier improves upon the stand-alone Viola-Jones when applying these features by reducing its false positives by 47% while retaining a similar computational cost and detection rate.
Recommended Citation
Maryan, Corey C., "Detecting Rip Currents from Images" (2018). University of New Orleans Theses and Dissertations. 2473.
https://scholarworks.uno.edu/td/2473
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.