Conversion of Image processing data of pipeline analysis using Machine learning Algorithm

Dr. A. Prema Kirubakaran

Bigdata, Cluster Analysis, Machine Learning, Prescriptive analytics, Supervised Learning.

In the field of image analysis and processing, the post section of having a data record plays a vital role. The research work carried out for analyzing a crack image in an oil pipeline titled “Image Analysis and processing using mathematical morphological operators and high frequency filter for pipeline crack measurement” had a difficult phase of saving the data for future study. To overcome this issue a technique to preserve the image data is handled with the concept of big data analysis. A pipeline scanned for quality maintenance sends numerous pictures, where the pixel data is converted to binary data and then these are calculated using the mathematical morphological operator based on erosion and corrosion of the images. These data are saved for further reference where the normal method of data saving using any hardware device was a big threat for loss of data and the cot to maintain the system is too high. To overcome this issue image data is been tried to save through the big data technique.

10.22161/ijaers/3.12.39