Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.
A mammography provides a grayscale image of the breast. The main challenge of analyzing mammography images is to extract the region boundary of the breast abnormality for further analysis. In computer vision, this method is also known as image segmentation. The variational level set mathematical model has been proven to be effective for image segmentation. Several selective types of variational level set models have recently been formulated to accurately segment a specific object on images. However, these models are incapable of handling complex intensity inhomogeneity images, and the segmentation process tends to be slow. Therefore, this study formulated a new selective type of the variational level set model to segment mammography images that incorporate a machine learning algorithm known as Self-Organizing Map (SOM). In addition to that, the Gaussian function was applied in the model as a regularizer to speed up the processing time. Then, the accuracy of the segmentation’s output was evaluated using the Jaccard, Dice, Accuracy and Error metrics, while the efficiency was assessed by recording the computational time. Experimental results indicated that the new proposed model is able to segment mammography images with the highest segmentation accuracy and fastest computational speed compared to other iterative models.
Redocumentation is an approach that is used to recover knowledge from raw software artifacts by using alternative presentations. Several legacy systems have been developed based on event-driven programming which require redocumentation. However, these existing repository and query techniques emphasize only on lexical and syntactical based queries which come with limitations in providing the semantic relationship for program understanding. We are using ontology based approach that uses both ontology reasoning and querying techniques to generate software documentation from the knowledge repository. We present a controlled experiment for the empirical evaluation on the proposed ontology based approach and implemented in a tool called Ontology Based Software Redocumentation (OBSR). In this experiment, two existing tools namely Universal Report (UR) and Microsoft Visual Studio specifically for Visual Basic (VB) programming environment have been selected to be compared with the OBSR tool. The goal is to provide experi-
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