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Detection of fish-food pellets in highly-cluttered underwater images with variable illumination Parsonage, Kevin David
Abstract
The focus of this research was to develop and test a method using image analysis to detect falling objects in a complex and variable underwater scene. This particular application involved the detection of fish-food pellets in netcage aquaculture systems. The problem was complicated due to the video camera positioning, number of other objects in the scene, and variable and uncontrollable background lighting conditions. The image analysis program was developed and tested using images obtained from industry standard video cameras. Testing conditions were as follows: Food pellet diameter: 2-11 mm. Water visibility: 3.5 - 11 m. - Fish size: 0.025 - 4.8 kg. - Fish stocking density: 0.27 - 20.3 kg/m³. The resulting image analysis program consisted of novel image enhancement and object recognition algorithms and was combined with filtering methods to eliminate false detections. The program was capable of detecting food pellet events providing the following conditions were met: 1) The camera view area was positioned within the sinking path of the food pellets. 2) The camera was positioned with its lens pointed towards the water surface. 3) The camera lens and rigging were clear of debris. 4) At least three food pellets of area 30 pixels or greater were present in the sampled images for 8 consecutive seconds.
Item Metadata
Title |
Detection of fish-food pellets in highly-cluttered underwater images with variable illumination
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2001
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Description |
The focus of this research was to develop and test a method using image analysis to
detect falling objects in a complex and variable underwater scene. This particular
application involved the detection of fish-food pellets in netcage aquaculture systems.
The problem was complicated due to the video camera positioning, number of other
objects in the scene, and variable and uncontrollable background lighting conditions.
The image analysis program was developed and tested using images obtained from
industry standard video cameras. Testing conditions were as follows:
Food pellet diameter: 2-11 mm.
Water visibility: 3.5 - 11 m.
- Fish size: 0.025 - 4.8 kg.
- Fish stocking density: 0.27 - 20.3 kg/m³.
The resulting image analysis program consisted of novel image enhancement and object
recognition algorithms and was combined with filtering methods to eliminate false
detections. The program was capable of detecting food pellet events providing the
following conditions were met:
1) The camera view area was positioned within the sinking path of the food pellets.
2) The camera was positioned with its lens pointed towards the water surface.
3) The camera lens and rigging were clear of debris.
4) At least three food pellets of area 30 pixels or greater were present in the sampled
images for 8 consecutive seconds.
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Extent |
11904686 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-09-22
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0058652
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2001-11
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.