A RESEARCH paper done by Universiti Tunku Abdul Rahman’s (UTAR) Faculty of Information and Communication Technology academic Assoc Prof Dr Lau Phooi Yee and Bachelor of Computer Science student Ling Yi Jun, stood out and was one of the best papers among 29 papers awarded.
Dr Lau and Ling’s paper was on “Fish Monitoring in Complex Environment”.
The researchers received the “Best Paper Award” at The International Workshop on Advanced Image Technology and The International Forum on Medical Imaging in Asia (IWAIT-IFMIA) 2019, held in Singapore.
IWAIT-IFMIA 2019 aims to provide an international platform for scientists, engineers, researchers and students to share and exchange research ideas in advanced image technology and medical imaging.
“We are happy to receive this award as we managed to compete with hundreds of papers submitted and being selected as one of the best papers.
“We spent six months preparing this research and Yi Jun spent a year prior to that on drafting and developing the system, working on the analysis and making improvements to the system,” said Dr Lau.
Ling too shared the same excitement with Dr Lau and expressed her happiness in receiving the award. “I’m grateful to my supervisor for all her guidance and for the opportunity given to me.
“The hard work has paid off,” said Ling.
The research explained that aquaculture farms provide a solution towards the overfishing phenomena. However, maintaining big scale farms manually requires going through hours of video footage to collect important information about the fish.
These footage are usually taken by an underwater camera affixed in many different ways within the farm’s cage and are manually analysed by human operators.
Since they are limited to biological restrictions, issues such as wandering attention span or human error may occur.
This paper proposes a non-intrusive and automated way of extracting meaningful information such as the number of fish from underwater and video footage of fish using image processing techniques. Experimental results show that the system managed to achieve 74.59% accuracy for correctly counting fish.
“Working in this area for 12 years, I saw great potential in this field, particularly in assisting the fish farm industry in reducing operational workload in monitoring their fish and automating the monitoring process,” said Dr Lau.
Ling said it was her interest in underwater image processing and the research’s possibilities of positively impacting the society that kept her motivated to complete the research.