Automated Stitching of Coral Reef Images and Extraction of Features for
Damselfish Shoaling Behavior Analysis
- URL: http://arxiv.org/abs/2006.15478v1
- Date: Sun, 28 Jun 2020 00:56:51 GMT
- Title: Automated Stitching of Coral Reef Images and Extraction of Features for
Damselfish Shoaling Behavior Analysis
- Authors: Riza Rae Pineda, Kristofer delas Pe\~nas, Dana Manogan
- Abstract summary: In marine ethology, the investigation of behavioral patterns in schooling species can provide supplemental information in the planning and management of marine resources.
Currently, damselfish species, although prevalent in tropical waters, have no adequate established base behavior information.
Visual marine data captured in the wild are scarce and prone to multiple scene variations, primarily caused by motion and changes in the natural environment.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Behavior analysis of animals involves the observation of intraspecific and
interspecific interactions among various organisms in the environment.
Collective behavior such as herding in farm animals, flocking of birds, and
shoaling and schooling of fish provide information on its benefits on
collective survival, fitness, reproductive patterns, group decision-making, and
effects in animal epidemiology. In marine ethology, the investigation of
behavioral patterns in schooling species can provide supplemental information
in the planning and management of marine resources. Currently, damselfish
species, although prevalent in tropical waters, have no adequate established
base behavior information. This limits reef managers in efficiently planning
for stress and disaster responses in protecting the reef. Visual marine data
captured in the wild are scarce and prone to multiple scene variations,
primarily caused by motion and changes in the natural environment. The gathered
videos of damselfish by this research exhibit several scene distortions caused
by erratic camera motions during acquisition. To effectively analyze shoaling
behavior given the issues posed by capturing data in the wild, we propose a
pre-processing system that utilizes color correction and image stitching
techniques and extracts behavior features for manual analysis.
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