A. Abbas Elmas is a PhD candidate and research assistant at Cukurova University. Currently working on 3D Reconstruction from UAV images.
PhD in Computer Engineering, 2019
MSc in Computer Engineering, 2019
BEng in Computer Engineering, 2014
KTO Karatay University
BEng in Mechanical Engineering, 2009
Turkish Military Academy
High School, 2008
Maltepe Military High School
In this thesis, currently available 3D mesh compression algorithms, frameworks, libraries etc. are investigated. Especially, the algorithms that are popular in survey papers but don’t have any implementation or had outdated implementation or no published version is available, are gathered together and compiled accordingly. According to the benchmark test results, current best general-purpose data compression methods are identified and applied as the last stage of mesh compression. Results are compared in order to demonstrate the current state of single-rate 3D mesh compression performance with the current best general-purpose data compression methods.
3D graphics are evolving media type used in all aspects of technological areas of today. Increase in demand on 3D graphics pushes technological advancements on 3D scan technology and approximation methods to next level which then results in more complex and highly detailed large 3D raw data. Thus, it is crucial to compress these graphics data efficiently. Over the last two decades, many algorithms have been proposed to compress these raw 3D data especially for compact storage, fast transmission, and efficient processing. Compression methods are branching among themselves. In this paper, 3D compression methods are summarized in a taxonomical fashion. A special attention is paid to the main ideas behind the single-rate compression algorithms and their contribution to 3D mesh compression technology. The advantages and the drawbacks of each algorithm are discussed to pave the road for the future 3D compression researchers.
Self-Organizing Maps (SOM), a type of Artificial Neural Network (ANN), is a data clustering tool that provides a way of representing multi-dimensional data in two-dimensional space. The maps are produced preserving topological relations between parameters of the input vectors. Unlike multi-layered feed forward neural networks, SOM employs unsupervised learning training mechanism. Interestingly, it requires no prior knowledge regarding the solution. The variety of the applications which employ SOM for data analysis reported in the literature is a clear indication of its acceptance as a powerful data analysis tool. SOM may, not only, present better viewing opportunities in such cases that displaying the relationships between the factors effecting the problem is impossible, but also, provides better exploration of the data. Application of SOM on the data collected in fisheries science provided enhanced outcomes and better understanding on the data collected. In this paper, SOM is discussed and reviewed in view of aquaculture and fisheries research based on the prevalence of isopods in the buccal cavity of one grouper species. The research was carried out to determine the seasonal patterns and potential impacts of the parasites on the goldblotch grouper using the SOM which were conducted in Iskenderun Bay.