Underwater video data are a rich source of information for marine biologists. However,
the large amount of recorded video creates a ’big data’ problem, which emphasizes
the need for automated detection techniques.
This work focuses on the detection of quasi-stationary crabs of various sizes in
deep-sea images. Specific issues related to image quality such as low contrast and
non-uniform lighting are addressed by the pre-processing step. The segmentation
step is based on color, size and shape considerations. Segmentation identifies regions
that potentially correspond to crabs. These regions are normalized to be invariant to
scale and translation. Feature vectors are formed by the normalized regions, and they
are further classified via supervised and non-supervised machine learning techniques.
The proposed approach is evaluated experimentally using a video dataset available
from Ocean Networks Canada. The thesis provides an in-depth discussion about the
performance of the proposed algorithms. / Graduate / 0544 / 0800 / 0547 / mars_mehr@hotmail.com
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/6439 |
| Date | 13 August 2015 |
| Creators | Mehrnejad, Marzieh |
| Contributors | Branzan Albu, Alexandra, Capson, David |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web, http://creativecommons.org/publicdomain/zero/1.0/ |
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