A Framework for Sea Breeze Front Detection from Coastal Regions of India Using Morphological Snake Algorithm

Abstract

Sea breezes are the most common winds experienced by people living in coastal regions. In general, the sea breeze is a flow of winds that particularly takes place in coastal areas. Sea breezes cause irregular climatic conditions. The salty air coming from the sea breeze has got many adverse effects like deterioration. The collision of two powerful sea breezes fronts can cause severe thunderstorms across the coastal regions. Therefore, it is important to know the location of the sea breeze front to define the regions affected by sea breezes. In order to detect the sea breeze front from satellite images, it is important to segment the satellite images. Image segmentation which helps in extracting the objects of interest and make the image more meaningful for further processing. Then using contour detection, the outline of sea breeze, which is sea breeze front can easily be extracted. A proper methodology with user interface is proposed in this paper for detecting the sea breeze front from the satellite images.

Classification Method of 3D Pattern Film Images Using MLP Based on the Optimal Widths of Histogram

“…The quality of 3D pattern film images can be inspected using rule-based methods or machine learning methods. Firstly, in the rule-based method, techniques have been proposed that classify images using the luminance or brightness of the image [1][2][3][4]. Among these methods, Lee and Kim published a study where they calculated the image histograms for 3D pattern film images, determined the width at the 10th percentile height, and then classified the images based on a threshold value [1].…”

“…Among the segmentation methods, there are techniques that allow for the morphological detection of objects, with morphological geodesic active contour being a prominent example. Morphological geodesic active contour is a method that combines the morphology snake method [4] and the geodesic active contour method [5], and this method progressively evolves the segmented regions before morphologically segmenting objects. Recently, studies have been published utilizing morphological geodesic active contour and image processing techniques to segment and classify welding beads for evaluating the performance of robots used in welding bead manufacturing [6], as a deep learning model based on a non-parametric adaptive active contour method called fast morphological geodesic active contour (FGAC) for segmenting the left ventricle [7], for automatic segmentation of the aorta from CT images using morphological geodesic active contour [8], and for segmenting lung images based on ACM without prior training using FGAC [9].…”

Inspection Algorithm of Welding Bead Based on Image Projection

“…The morphological geodesic active contour method is a combination of the morphological snakes introduced in [19] and the geodesic active contour in [20]. This method segments the area of interest by expanding the gradually developing contour.…”

“…Furthermore, a limitation of using ultrasonic inspection is that undetectable defects may arise depending on the shape or thickness of the welding bead. Recently, various methods have been proposed to analyze the quality of welding beads using image processing techniques such as segmentation and machine learning [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Segmentation algorithms are utilized to detect and inspect only the welding bead areas in images.…”