Machine vision is very important for ensuring the success of intelligent transportation systems, particularly in the area of road maintenance. For this reason, many studies had been focusing on automatic image-based crack detection as a replacement for manual inspection that had depended on the specialist's knowledge and expertise. In the image processing technique, the pre-processing and edge detection stages are important for filtering out noises and in enhancing the quality of the edges in the image. Since threshold is one of the powerful methods used in the edge detection of an image, we have therefore proposed a modified Otsu-Canny Edge Detection Algorithm in the selection of the two threshold values as well as implemented a multi-resolution level fixed partitioning method in the analysis of the global and local threshold values of the image. This is then followed by a statistical measure in selecting the edge image with the best global threshold. This study had utilized the road crack image dataset that were obtained from Crackforest. The results had revealed the proposed method to not only perform better than the conventional Canny edge detection method but had also shown the maximum value derived from the local threshold of 5x5 partitioned image outperforming the other partitioned scales.
Numerous decisions in our daily lives require the ability to calculate, solve problems, and employ concepts and skills in mathematics. However, students always have bad perceptions in mathematics as this subject is often challenging for students to master. Reports on issues about students' mathematical education have been raised by many mathematics educators. Among of the issues are about difficulties in understanding mathematical concepts, attitudes towards mathematics, low achievement in mathematics and many more. Hence, the aim of this study is to explore students' perceptions and attitudes towards mathematics by analyzing the impact of teaching application of mathematics to the students. The data were collected from Computer Science students where each participant was assigned to complete self-reported and close ended questionnaire. In this study, examination on attitudes of student include engagement, confidence and importance of mathematics. Data were analyzed by measuring the difference of individual statements on percentage of agreeing in pre -Smart for mathematics and post -Smart for mathematics. The findings show that when students learned and exposed to the application of mathematics, their engagement, confidence and importance of mathematics increases. It can be concluded that by exposing students the purpose of learning mathematics in computer science, students' positive attitude towards mathematics can be developed and hence improve their interests in learning mathematics.
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