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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.

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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.

Abstract. Machine vision calls for the use of detectors to ascertain the features and type of object portrayed in the image. The employment of unmanned aerial vehicles (UAVs), which can function freely in active and precarious settings, is currently gaining momentum. These vehicles are mainly used for the detecting, classifying and tracking of an object. However, the achievement of these objectives necessitates the involvement of an effective edge detection procedure. Sobel, Canny, Prewitt and LoG are among the many edge detection procedures presently available. In this endeavour, we opted for the utilization of UTeM UAVs images for an evaluation of these edge detection procedures. During our investigations, the ground truth edge images were corroborated by a specialist in this field. The results obtained from these investigations revealed that in terms of accuracy, precision, sensitivity and f-measure, the Prewitt procedure outperforms the other methods mentioned.

<span lang="EN-GB">Binarization</span><span lang="EN-GB"> of historical documents nowadays is very important as digital archiving has become the best and preferred solution for the retrieval and storage of valuable archives. However, the process becomes more challenging due to the degradation of historical documents. Hence, this paper described a method on binarization of historical documents using the learning concept. Support vector machine (SVM) learning was used as a classifier in this work. After training some images with the help of ground truth images, a model was developed. Testing images then used the model to segregate each pixel as text or non-text. The grey level and RGB values were chosen as descriptors for a particular pixel and comparisons were made between these two descriptors. The intensities of the local neighbourhood for every pixel were used in the experiment. To compare these descriptors, standard dataset HDIBCO2014, DIBCO2012 and DIBCO2016 were used in the training and testing phase. The results from the experiment clearly showed that grey level values gave better performance compared to RGB values.</span>

Extrema points are usually applied to solve everyday problems, for example, to determine the potential of a created tool and for optimisation. In this study, extrema points were used to help determine the region of interest (ROI) for the iris in iris recognition systems. Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on the images of one or both irises of an individual's eyes, where the complex patterns are unique, stable, and can be seen from a distance. In order to obtain accurate results, the iris must be localised correctly. Hence, to address this issue, this paper proposed a method of iris localisation in the case of ideal and non-ideal iris images. In this study, the algorithm was based on finding the classification for the region of interest (ROI) with the help of a Support Vector Machine (SVM) by applying a histogram of grey level values as a descriptor in each region from the region growing technique. The valid ROI was found from the probabilities graph of the SVM obtained by looking at the global minimum conditions determined by a second derivative model in a graph of functions. Furthermore, the model from the global minimum condition values was used in the test phase, and the results showed that the ROI image obtained helped in the elimination of sensitive noise with the involvement of fewer computations, while reserving relevant information.