Recent progress in animal biometrics has revolutionized wildlife research. Cutting-edge techniques allow researchers to track individuals through noninvasive methods of recognition that are not only more reliable, but also applicable to large, hard-to-find, and otherwise difficult to observe animals. In this thesis, we propose a framework for automated individual shark identification based on semantic segmentation, boundary descriptors and bipartite perfect matching applied to shark dorsal fins. In order to identify a shark, we first apply semantic segmentation to extract the dorsal fin of the input source, then transform the fin’s contour to a normalized coordinate system so that we can analyze images of sharks regardless of orientation and scale. Finally, we propose a metric scheme that performs a minimum weight perfect matching in a bipartite graph. The experimental results show that our metric is able to identify and track individuals from visual data.
|Advisor:||Moon, Ju Cheol|
|Commitee:||Morales Ponce, Oscar, Lam, Shui|
|School:||California State University, Long Beach|
|Department:||Computer Engineering and Computer Science|
|School Location:||United States -- California|
|Source:||MAI 82/3(E), Masters Abstracts International|
|Subjects:||Bioinformatics, Artificial intelligence|
|Keywords:||Biometric, Dorsal, Fin, Identification, Shark|
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