Tracking the Motion of Box Jellyfish
(2014) In Master's Theses in Mathematical Sciences FMA820 20131Mathematics (Faculty of Engineering)
- Abstract
- This master’s thesis investigates the possibilities of detecting the rhopalia of box jellyfishes, i.e. the eyes of the box jellyfish. Each box jellyfish has four rhopalia and each of the rhopalium consists of six light sensing eyes which they use to interpret the surroundings. In this project we have worked with fifteen film sequences all consisting of different box jellyfishes and recorded under different light settings. The framework for detecting the rhopalia is divided into three parts, in order; detection, clustering and tracking. The input in the detection step is the grayscale image of the box jellyfish and in the output possible rhopalia are marked as detections. These detections are then sent into the clustering step which filters... (More)
- This master’s thesis investigates the possibilities of detecting the rhopalia of box jellyfishes, i.e. the eyes of the box jellyfish. Each box jellyfish has four rhopalia and each of the rhopalium consists of six light sensing eyes which they use to interpret the surroundings. In this project we have worked with fifteen film sequences all consisting of different box jellyfishes and recorded under different light settings. The framework for detecting the rhopalia is divided into three parts, in order; detection, clustering and tracking. The input in the detection step is the grayscale image of the box jellyfish and in the output possible rhopalia are marked as detections. These detections are then sent into the clustering step which filters out the noise in the picture and saves clusters of detections. The reason for saving these clusters is because a rhopalium appears as a dark disc with a radius of around 13 pixels and thus should produce many detections. In the final step, four clusters are selected from all clusters as the correct rhopalia. The choice has been made to focus on the detection step in this thesis, leaving the cluster and tracking step with only one algorithm each. A combination of a detection method, a clustering- and a tracking algorithm is called a system.
To be able to detect a rhopalium, a set of data points, a pattern, is used to compare values (light intensity) in order to capture the appearance of a rhopalium. The more data points the longer the execution time so this needs to be done with minimal amount of data points but still enough to capture the visual aspect of the rhopalium.
The best performing systems have patterns of a small disc inside the rhopalium and a larger circle just outside. The values of the pixels outside should be greater (brighter) than the ones inside. But because of some artefacts and noise in the pictures not all need to be darker, instead, a threshold is used, e.g 78 % of the pixel values outside should be greater than the ones inside. This results in a accuracy of 98 %. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/4463255
- author
- Kjellberg, Tobias LU
- supervisor
-
- Tobias Palmér LU
- Magnus Oskarsson LU
- Karl Åström LU
- organization
- course
- FMA820 20131
- year
- 2014
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- image analysis, tracking
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3260-2014
- ISSN
- 1404-6342
- other publication id
- 2014:E26
- language
- English
- id
- 4463255
- date added to LUP
- 2014-07-04 17:26:55
- date last changed
- 2014-07-04 17:26:55
@misc{4463255,
abstract = {{This master’s thesis investigates the possibilities of detecting the rhopalia of box jellyfishes, i.e. the eyes of the box jellyfish. Each box jellyfish has four rhopalia and each of the rhopalium consists of six light sensing eyes which they use to interpret the surroundings. In this project we have worked with fifteen film sequences all consisting of different box jellyfishes and recorded under different light settings. The framework for detecting the rhopalia is divided into three parts, in order; detection, clustering and tracking. The input in the detection step is the grayscale image of the box jellyfish and in the output possible rhopalia are marked as detections. These detections are then sent into the clustering step which filters out the noise in the picture and saves clusters of detections. The reason for saving these clusters is because a rhopalium appears as a dark disc with a radius of around 13 pixels and thus should produce many detections. In the final step, four clusters are selected from all clusters as the correct rhopalia. The choice has been made to focus on the detection step in this thesis, leaving the cluster and tracking step with only one algorithm each. A combination of a detection method, a clustering- and a tracking algorithm is called a system.
To be able to detect a rhopalium, a set of data points, a pattern, is used to compare values (light intensity) in order to capture the appearance of a rhopalium. The more data points the longer the execution time so this needs to be done with minimal amount of data points but still enough to capture the visual aspect of the rhopalium.
The best performing systems have patterns of a small disc inside the rhopalium and a larger circle just outside. The values of the pixels outside should be greater (brighter) than the ones inside. But because of some artefacts and noise in the pictures not all need to be darker, instead, a threshold is used, e.g 78 % of the pixel values outside should be greater than the ones inside. This results in a accuracy of 98 %.}},
author = {{Kjellberg, Tobias}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master's Theses in Mathematical Sciences}},
title = {{Tracking the Motion of Box Jellyfish}},
year = {{2014}},
}