Research Publications

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Embargo
Interactive Game Application for Garbage Disposal using Image Processing and Location Based Services
(IEEE, 2018-12-21) Nanayakkara, P; Rathnaweera, T; Weerasingha, S; Pieris, Y
Waste disposal is one of the major contemporary topics which is being discussed globally. The main reason for this issue to have such prominence is due to the fact that people are not aware how far-reaching this topic has become. In this paper we propose ScrapWrap, an interactive game for garbage disposal where the main objective is to make people aware how imperative it is to dispose garbage and assist people to have a change of attitude towards this complication. In Scrapwrap, people will be awarded considering several facts such as for notifying littered areas, for cleaning the area, for verifying the cleaned area, and for arranging cleaning campaigns. A chat-based community is created within ScrapWrap so that players could interact with each other, and this strategy is useful when arranging campaigns or scavenger hunt activities to clean the waste. An assistive chat bot is introduced to solve the problems a player might have, and to remind the event details and guide the player throughout the game flawlessly. The long-term goal of introducing Scrapwrap is to let people be aware of the importance of garbage disposal in an interactive manner and to improve the cleanliness of the surroundings.
Embargo
Smart Platform for Film Shooting Management
(IEEE, 2019-12-06) Senarath, S. M. M. M; Perera, M. T. K; Viduranga, D. G. R; Wijayananda, H. M. C. S; Rankothge, W
Producing a movie involves difficult and time-consuming phases, specially, pre-production and production. It's a challenging task to find out suitable locations for each scene and building a schedule without any clashes. We have proposed and implemented a platform for film shooting management with following modules: (1) identify required background for each scene, (2) classify available film shooting locations, (3) compare the required background and available film shooting locations and (4) schedule the shooting of each scene. We have used natural language processing, image processing, string matching algorithms and optimization techniques to implement the above-mentioned modules. Our results show that, using our proposed modules, the film shooting management related services can be automated efficiently and effectively.
Embargo
DFOG-Image Processing Application for Real-Time Defogging
(IEEE, 2020-11-04) Indiketiya, I. H. O. H; Kulasekara, K. M. R. A; Thomas, J. M; Gamage, I; Thilakarathna, T
The enhancement of real-time video taken under bad visibility or bad weather is a vital necessity in consumer transport industry and computer vision applications. During the past decade, many researchers have been devoted to the problem of how to remove fog noise from real-time video. Nowadays vehicle industry uses various computing systems to assist in the transport of travelers from one location to another .now most of the cars have revers camera front cameras and sensors who give the signal when the vehicle is near to another object. These detections and identification are useful for the safe operation of vehicles. When looking through vehicle accident history, many accidents caused bad weather conditions. Fog, haze, rain, and other natural weather conditions cannot remove physically. Fog and haze block vison above 1 kilometer. There is a defogger in the windscreen, but it is only removed Mist on the windscreen. For the driver's vision above the rode, there is no such thing for that. The purpose of this research paper is introducing a new system to remove fog from real-time video and give detailed visual to the driver in foggy or other bad weather condition. This D-Fog system includes functions such as give clear realtime visual in bad weather condition, recognize, and give details about the object above the road, give the distance between objects and vehicle. In this system, main function is producing real-time defogged, clear video. Combination of Ha and Hoon method and Dark channel priority method used to get this real-time defog video. To recognize the object, this system has use thermal sensors and heat maps. To get the distance between object and vehicle this system has use LIDAR sensors. Because of this facility, we can name this system as three in one system.
Embargo
Coconut Disease Prediction System Using Image Processing and Deep Learning Techniques
(Institute of Electrical and Electronics Engineers Inc., 2020-12-09) Nesarajan, D; Kunalan, L; Logeswaran, M; Kasthuriarachchi, S; Lungalage, D
Coconut production is the most important and one of the main sources of income in the Sri Lankan economy. The recent time it has been observed that most of the coconut trees are affected by the diseases which gradually reduces the strength and production of coconut. Most of the tree leaves are affected by pest diseases and nutrient deficiency. Our main intensive is to enhance the livelihood of coconut leaves and identify the diseases at the early stage so that farmers get more benefits from coconut production. This paper proposes the detection of pest attack and nutrient deficiency in the coconut leaves and analysis of the diseases. Coconut leaves monitorization has been taken place after the use of pesticides and fertilizer with the help of the finest machine learning and image processing techniques. Rather than human experts, automatic recognition will be beneficial and the fastest approach to identify the diseases in the coconut leaves very efficiently. Thus, in this project, we developed an android mobile application to identify the pests by their food behaviors, pest diseases and the nutrition deficiencies in the coconut trees. As an initial step, all datasets for image processing technology met pre-processing steps such as converting RGB to greyscale, filtering, resizing, horizontal flip and vertical flip. After completing the above steps, the classification was performed by analyzing several algorithms in the literature review. SVM and CNN were chosen as the best and appropriate classifier with 93.54% and 93.72% of accuracy respectively. The outcome of this project will help the farmers to increase the coconut production and undoubtedly will make a revolution in the agriculture sector.
Open Access
BIOMETRIC SMART SECURITY SYSTEM WITH CHILD CARE FOR A SMART SOCIETY
(IET- Sri Lanka Network, 2019) Lokuliyana, S; Mundigala, I. U; Sanjeewa, G. H. A
This research is mainly focused on Infant movement detection and alerting, in order to enhance their security within the home premises. As the first move, the research focuses on the identification of the human and classifying whether an adult or a baby. Then a model was built up in three classifications to identify static and dynamic positions of the infant, through Image Processing and analysis. In order to enhance the accuracy of the custom classifiers an already trained model using 1 million image set was retrained by customized image sets. To present this research as a smart home solution modern technology were used in implementing the close connection between the infant and the parent.
Embargo
CURETO: Skin Diseases Detection Using Image Processing And CNN
(IEEE, 2020-11-17) Karunanayake, R. K. M. S. K; Dananjaya, W. G. M; Peiris, M. S. Y; Gunatileka, B. R. I. S; Lokuliyana, S; Kuruppu, A
Busy lifestyles these days have led people to forget to drink water regularly which results in inadequate hydration and oily skin, oily skin has become one of the main factors for Acne vulgaris. Acne vulgaris, particularly on the face, greatly affects a person's social, mental wellbeing and personal satisfaction for teens. Besides the fact that acne is well known as an inflammatory disorder, it was reported to have caused serious long-term consequences such as depression, scarring, mental illness, including pain and suicide. In this research work, a smartphone-based expert system namely “Cureto” is implemented using a hybrid approach i.e. using deep convolutional neural network (CNN) and natural language processing (NLP). The proposed work is designed, implemented and tested to classify Acne density, skin sensitivity and to identify the specific acne subtypes namely whiteheads, blackheads, papules, pustules, nodules and cysts. The proposed work not only classifies Acne Vulgaris but also recommends appropriate treatments based on their classification, severity and other demographic factors such as age, gender, etc. The results obtained show that for Acne type classification the accuracy ranges from 90%-95% and for Skin Sensitivity and Acne density the accuracy ranges from 93%-96%.
Open Access
A mobile base application for cataract and conjunctivitis detection
(University of Kelaniya, 2020) Soysa, A; De Silva, D. I
With time the life patterns of humans have evolved at a rapid space. Today, it has come to a point where people are opting to put their health status behind other priorities in life. A contemporary example is the spreading of the COVID-19 virus. One of the other significant health issues faced by the present-day community is illnesses related to the eyes. However, unlike other health issues, most of the eye diseases can be cured with proper attention. Cataract and Conjunctivitis are identified as two of the main eye diseases faced by a mass amount of people around the world. If left untreated, these diseases can even lead to blindness. As a matter of fact, Cataract has been reported as the first cause of blindness by the world health organization. Typically, the detection of these diseases is done by an ophthalmologist with the use of a special medical equipment. Thus, the channeling of an ophthalmologist has become a mandatory requirement for the detection of these diseases. In addition, the availability of medical equipment and medical officers is deficient in rural areas. Thus, as a solution for the above-mentioned issues, it was decided to propose a mobilebased application, Eye Plus, for the detection of Cataract and Conjunctivitis diseases. Using Eye Plus, one would be able to test his/her eyes at a convenient time in any place for a zero cost. In addition, it provides additional information related to Cataract and Conjunctivitis diseases. Another special feature of the application is the ability to operate it without the help of another party. At present, the application achieved a success rate of 83.3% for a collection of 150 images.
Embargo
“SenseA”-Autism Early Signs and Pre-Aggressive Detector Through Image Processing
(IEEE, 2017-12-04) Gamaethige, C; Gunathilake, U; Jayasena, D; Manike, H; Samarasinghe, P; Yatanwala, T
This paper presents an efficient solution for the current problem of identifying early signs of autism and detecting pre aggressive behaviours using videos which can be used to produce a more convenient environment for autistic children and their caregivers. Early detection of autism spectrum disorder and its consequences play major role in intervention. Yet it often remains unrecognized and diagnosed in non-clinical environments because of unawareness and the lack of screening tools specific to the autism. At times, autistic children express their feelings through aggressive behaviour towards themselves or other children due to number of reasons such as failing to understand their own feelings, misunderstanding and severe distress. While an autistic child engages in physical aggression, an immediate response is required because the sibling or peer will likely react to the child's aggressive behaviour. There's no systematic approach to identify the pre-aggressive behaviour of autistic children in software engineering perspective. The proposed Autism Spectrum Disorder (ASD) early signs and pre-aggressive detection system is a software which provides automated solution to aforementioned problems using computer vision and machine learning libraries. It provides a level detection on early signs of autism by analyzing facial features and behaviour patterns in non-clinical perspective. Finally it detects the pre aggressive behaviours of autistic children and alerts the relevant authorized individual using a mobile notification.
Open Access
Animal Classification System Based on Image Processing & Support Vector Machine
(Scientific Research Publishing, 2016-01-15) Seneviratne, L; Shalika, A. W. D. U
This project is mainly focused to develop system for animal researchers & wild life photographers to overcome so many challenges in their day life today. When they engage in such situation, they need to be patiently waiting for long hours, maybe several days in whatever location and under severe weather conditions until capturing what they are interested in. Also there is a big demand for rare wild life photo graphs. The proposed method makes the task automatically use microcontroller controlled camera, image processing and machine learning techniques. First with the aid of microcontroller and four passive IR sensors system will automatically detect the presence of animal and rotate the camera toward that direction. Then the motion detection algorithm will get the animal into middle of the frame and capture by high end auto focus web cam. Then the captured images send to the PC and are compared with photograph database to check whether the animal is exactly the same as the photographer choice. If that captured animal is the exactly one who need to capture then it will automatically capture more. Though there are several technologies available none of these are capable of recognizing what it captures. There is no detection of animal presence in different angles. Most of available equipment uses a set of PIR sensors and whatever it disturbs the IR field will automatically be captured and stored. Night time images are black and white and have less details and clarity due to infrared flash quality. If the infrared flash is designed for best image quality, range will be sacrificed. The photographer might be interested in a specific animal but there is no facility to recognize automatically whether captured animal is the photographer’s choice or not.
Embargo
Real-Time Greenhouse Environmental Conditions Optimization Using Neural Network and Image Processing
(IEEE, 2020-11-04) Wickramaarachchi, P; Balasooriya, N; Welipenne, L; Gunasekara, S; Jayakody, A
Agricultural business is one of the biggest areas in world economy. With the growth of population losing agricultural lands is the major issue in world food production. Therefore, controlled environment agricultural systems under vertical farming have been introduced with greenhouses. Within greenhouses there is not a mechanism to continuously monitor the growing community and change the climate conditions. Existing systems only predict the required conditions for the plant and once predicted that value is provided to the plants continuously or change the values from season to season. To address these issues, a working prototype of an IoT based smart hydroponic system is introduced, which uses computer vision to gain maximum profits by growing a specific cultivation by providing endemic environmental conditions and addressing the problems over its growing process. There, this research presents a way of external environmental condition optimization. Regression type Feed Forward Neural Network is considered for this research to optimize the required conditions for tomato plants. Based on the current height of the plant, expected height for next 24 hours, and growth date of the plants neural networks predict the CO2, temperature and humidity level for next 24 hours with the accuracy of 88.33%, 89.21% and 92.65% respectively. The objectives of the research can be achieved by this retrieved results. The successful implementation of neural networks results a cost-effective modern farming solution for growers. This research will be supportive to attain a fundamental comprehension on the concept of the research area.

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