SLIIT Conference and Symposium Proceedings

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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.

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Subject: search.filters.subject.Augmented Reality

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    Easy Learning: Augmented Reality Based Environmental Studies for Primary Students
    (IEEE, 2019-12-05) Wickramapala, T; Jayawardhana, L; Tharaki, S; Senevirathna, S; Gamage, N; Wickramarathna, J
    Primary education is every child's fundamental right. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), primary schooling offers learning and educational activities typically intended to provide learners with basic abilities in reading and writing. The students find it difficult to identify trees and animals around them due to the lack of exposure to the natural environment. This research study introduces mobile based application (Easy Learning) which embraced augmented reality technology (AR) to motivate and aid learners in studying Environmental Studies in terms of identification of animals and trees. In order to provide sufficient knowledge about trees and animals, this research focuses on safe internet browsing and summarization for trees and animals. Easy learning suggest safe videos for kids and generates knowledge based questions to evaluate themselves as well. The study also evaluates whether the students like the features of the Easy Learning and the rate of knowledge change, through pre and post questionnaires given at the beginning and at the end of the implementation of Easy Learning. The findings proves that the Easy Learning as an interactive AR based learning instrument, for Environmental Studies which improves the learning curve.
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    An Enhanced Virtual Fitting Room using Deep Neural Networks
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ileperuma, I.C.S.; Gunathilake, H.M.Y.V.; Dilshan, K.P.A.P.; Nishali, S.A.D.S.; Gamage, A.I.; Priyadarshana, Y.H.P.P.
    As the customer's experience in present fit-on rooms is considered as an essential part of the textile industry, these fit-on rooms play a huge role in the textile shops. It is quite an arduous method and generates problems like long queues, having to change clothes individually, privacy problems and wasting time. The proposed convolutional neural network-based Virtual Fit-on Room helps to prevent the above mentioned problems. This product contains a TV screen, two web cameras, and a PC. It captures the customer's body by using two web cameras and displays the customer's dressed body. The combination of CNN in Deep learning and AR processes the body detection and generates the customer's dressed object. The application uses the stereo vision concept to get body measurements. The system detects customer age, gender, face type, and skin tones which are used to recommend cloth styles to customers. Another requirement of this system is customizing styles according to the customer requirements and suggests different styles of clothes. The system achieved 99% accuracy when suggesting different styles using FFNN. Customers can choose clothes for another person who does not physically appear with the customer in the textile shop. The expected output delivers the most realistic dressed object to the customer which allows the efficient customizations for the textile products according to customer requirements. This product can highly influence the textile and fashion industry. Therefore, this product is suitable to compete with other applications in the industry.