Shape Characterization on Turtle Detection
| dc.contributor.author | Sunardi | |
| dc.contributor.author | Yudhana, Anton | |
| dc.contributor.author | Nasir, Nur Atika Nadhira Mohd | |
| dc.contributor.author | Naharuddin, Noor Zirwatul Ahlam | |
| dc.contributor.author | Mahfurdz, Azrul | |
| dc.date.accessioned | 2015-10-31T02:11:33Z | |
| dc.date.available | 2015-10-31T02:11:33Z | |
| dc.date.issued | 2015-07-30 | |
| dc.identifier.citation | 1. Red List of International Union for Conservation of Nature (ICUN), 2004 2. Sunardi, Noor Zirwatul Ahlam, Azrul Mahfurdz, Anton Yudhana, 2013. Prevent Turtle Trap in the Fishing Gear Using Ultrasound, IEEE Conference on Industrial Electronics andApplications, Melbourne, 19-21 June 2013 3. The Humane Society of the United States, September 2005, Turtle Excluder Device (TED). 4. Azrul Mahfurdz, Sunardi, Hamzah Ahmad, 2013. Acoustic Strength of Green Turtle and Fish Based on FFT Analysis, International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 9, 2013 5. G. Schofield, 2008. Identification Tree: Group Assignation of the right facial scales 6. Food and Agriculture Organization Of The United Nations Rome, Guidelines To Reduces Sea Turtle Mortality, 2009. 7. Bastiaan J. Boom, Phoenix X. Huang, Cigdem Beyan, 2008. Long-term underwater camera surveillance for monitoring and analysis of fish populations. 8. National Marine Fisheries Service and the Atlantic States Marine Fisheries Commission, 2013. Workshop on Sea Turtle and Atlantic Sturgeon Bycatch Reduction in Gillnet Fisheries. 9. Jeanette Wyeken, December 2001. The Anatomy of Sea Turtles. 10. Chong Soon, Tan Phoo Yee Lau, Detect of marine species on under water video image, 2014. 11. R.N.Williams, A.F.Kelsall, T.J.Lambert, T.Pauly, 2006. Detecting Marine Animals in Underware Video. 12. Mark R.Shortis, Mehdi Ravanbakskh, Faisal Shafait, A review of techniques for identification and measurement of fish in underwater stereo-video image sequences, 2010. | in_ID |
| dc.identifier.issn | 2339-028X | |
| dc.identifier.uri | http://hdl.handle.net/11617/6206 | |
| dc.description.abstract | All sea turtles are considered as endangered and threatened that is affected by some natural and other caused by human activities. One of the problems faced on fishing operation is caught on gill net. Turtle Excluder Device (TED) is developed to prevent turtle approaches the net by dispels the sound signal. The objectives of this research project is to develop a system for identify the presence of turtle by using shape characterization. Turtle identification deployed by using underwater video camera with implementation of MATLAB software. The Green turtle (Chelonia Mydas) for various ages are used. The parameters are the head, tail, flippers and carapace of orientation. The characterization of parameters identified based on the analysis on image processing. The shape parameters of flippers are shown as the most identified. The orientation front and side easily to recognize the turtle present. In addition, for detection of turtle should over than 5000 number of pixels is identified as a turtle. This information will used to identify the turtle and prevent caught in the fishing net using improvement of TED with sound signal and image detection. Therefore, the endangered turtle can be protected and fisherman also can improve their efficiency and help to safeguard marine ecosystems. | in_ID |
| dc.language.iso | en | in_ID |
| dc.publisher | Universitas Muhammadiyah Surakarta | in_ID |
| dc.subject | Image Processing | in_ID |
| dc.subject | Shape | in_ID |
| dc.subject | Turtle | in_ID |
| dc.subject | TED | in_ID |
| dc.subject | Underwater | in_ID |
| dc.title | Shape Characterization on Turtle Detection | in_ID |
| dc.type | Article | in_ID |
