Recently, interest in volunteerism focused on environmental sustainability has grown dramatically due to the alarming environmental issues such as global warming, foreign waste and greenhouse gases effects. A greenery club, namely Kelab Bumi Hijau (KBH) which consists of volunteering students led by Majlis Felow Kolej Tuanku Canselor (MFKTC), Universiti Teknologi Malaysia (UTM) has been set up in 2017 to monitor and improve the greenery particularly within Kolej Tuanku Canselor (KTC). Several activities have been carried out by KBH such as picking up litter, clearing unwanted shrubs and planting trees. This study is carried out to evaluate the relevance of KBH to the greenery of KTC. In addition, the challenges faced by KBH and their future plans are also highlighted. The results show that the greenery club namely KBH has not only enhanced the greenery and environment of KTC, but also cultivated volunteerism among the students. Although the data presented apply only to KTC, we believe that the volunteerism to sustain the greenery should be carried out in all residential college in UTM.
Diabetic retinopathy is a common eye disease among diabetic patients which is caused by excessive sugar in the blood vessels that damage the retina. Fundus images are retina images that are captured and diagnosed by ophthalmologists. Ophthalmologists diagnose the progressive stages of diabetic retinopathy so that early detection of pre-diabetic retinopathy can be carried out. However, the quality of the fundus image can be associated with the brightness of the background and the indistinctive vessel contrast. This paper presents a novel extension of Bi-histogram Bezier curve contrast enhancement (BBCCE) based on the mean partition of its histogram. The disadvantage of having mean as the threshold partition is that the histogram distribution can be skewed due to an outlier. The proposed Dualistic Sub-Image Bi-histogram Bezier Curve Contrast Enhancement (DSI-BBCCE) method partitions the original histogram into two, using the median of the active dynamic intensity range of the input image and process two Bezier transform curves separately to replace the original cumulative density function curve as the median is not affected by the outlier. This DSI-BBCCE has the advantage of preserving the structure, median brightness and preventing over enhancement. The result shows that DSI-BBCCE has achieved a power signal to noise ratio (PSNR) of 20.08±0.94 dB, absolute mean brightness error (AMBE) of 20.15±1.89, structural similarity index model (SSIM) of 0.8096±0.0185, structure measure operator (SMO) of 3.2±1.10 and lightness measure order (LMO) of 200.90±44.19.
In computer vision, edge detection is a crucial step in identifying the objects' boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true nonedge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy.
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