Development of an underwater machine vision for coral detection
Date of Publication
2016
Document Type
Master's Thesis
Degree Name
Master of Science in Electronics and Communications Engineering
Subject Categories
Electrical and Computer Engineering
College
Gokongwei College of Engineering
Department/Unit
Electronics and Communications Engineering
Thesis Adviser
Elmer P. Dadios
Defense Panel Chair
Edwin Sybingco
Defense Panel Member
Lawrence A. Gan Lim
Angel A. Bandala
Abstract/Summary
The proposed research study entitled Development of an Underwater Machine Vision for Coral Detection focuses on the utilization of machine vision for underwater assessment, specifically, on coral monitoring and detection. The machine vision system is intended to be employed in an automated underwater vehicle through the use of an underwater camera and light sources to achieve optimum image acquisition and capture. Moreover, said system will be connected through cables (tethered) so that it can also be controlled remotely by the user. Algorithms for image acquisition, pre-processing, processing, image segmentation and decision making will be used in order to achieve an accurate, reliable and consistent underwater machine vision system. The system functionality was verified based on its accuracy of detection. Using Cascaded Algorithm with merge detection an overall system accuracy of 80% was obtained while without merge detection it 72% accurate. Based on Cohen’s Kappa measurement both implementation (with merge detection and without merge detection) have 0.44 and 0.59 kappa measurement respectively which means there is a moderate agreement between the input and output parameters.
Abstract Format
html
Language
English
Format
Electronic
Accession Number
CDTG006885
Shelf Location
Archives, The Learning Common's, 12F Henry Sy Sr. Hall
Physical Description
1 computer optical disc; 4 3/4 in.
Keywords
Underwater vision
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Recommended Citation
Purio, M. C. (2016). Development of an underwater machine vision for coral detection. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/7289
Embargo Period
10-17-2024
