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Traffic congestion has become a serious issue due to the growing number of vehicles in Malaysia. Traffic light control system is widely used to control the flow of road junction. Currently, most of the traffic light system used pre-time and count down timers to control traffic flow. Due to the fixed-time setting, often the system unable to handle unexpected heavy traffic flows and cause traffic jam. Thus, there is a need of adaptive traffic signals which are able to do real time monitoring to control traffic light signal based on traffic density. This study proposed an adaptive traffic light control system that uses image processing and image matching technique in controlling the traffic in an effective manner by taking images of each lane at a junction. The density of traffic in the images at each junction are compared. Results show that more time are allocated for the vehicles on the densest road to pass compared to other less dense road. Edge operation detector is used to detect the density of traffic at each lane. In this study, a comparison study was carried out by applying five different edge detectors namely Roberts, Sobel, Log, Canny and active contour. Among these detectors, Canny edge operation detector has found to be the best as it could extract actual edges with average time of 0.453 second.

In aquaculture industry, measurement of fish length plays an important role in monitoring and determining fish growth, gender, age and reproduction. Currently, fish rearing industries are using measuring wooden board or acrylic plastic with the scale attached to the board itself. However, this method is causing a stress on the fish when measuring their length alive. Furthermore, manual measurement of fish length one by one using measurement board is time-consuming. Hence, this research presents an automated system that is able to measure fish length specifically tilapia fish by using image processing technique and Graphical User Interface (GUI). This automated GUI system is developed for end user and it is able to measure the length of tilapia automatically and accurately. The system is designed by using image processing tool, which consists of image segmentation and feature extraction to get the fish-shaped region. Based on the results, the GUI system is able to measure automatically and accurately the fish length based on the fish image. This developed system resulting with a 8.50% mean relative error and 91.50% accuracy of the tilapia fish length measurement for a 20 cm distance between the digital camera and the tilapia fish. These results show that the proposed system is capable to produce faster results and yield accurate length measurement of the tilapia fish. Thus, with this automated system, it is potentially assisting in fish reproduction and growth for aquaculture industry especially for tilapia industry.