STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Image Processing & Pattern Recognition Progress

Object’s Area Measurement using Image Processing

  1. Riya Gautam

Abstract

Object measurement holds substantial significance across a wide spectrum of domains, spanning from quality control in manufacturing to applications in healthcare and environmental monitoring. In this study, a method is suggested to detect the object and calculate the area of the given object in real time using MATLAB. Also, comparing the result obtained with the known area and seeing how accurate the area calculated is. The proposed method for detecting objects and calculating the area is reliable and accurate compared to the area calculated is verified with the manual area calculation readings. A methods is suggested without any constraint of the height from which the object is captured. In this method, the object whose area is to be measured is kept on a white backgroung of known area, the backgroud edges are etracted, the image is thresholded such that the white background has pixel value 255 and the object to be measured has pixel value 0, the image is filled and thus the pixels values initially having 0 intensity now possess intensity level of 255, further, the pixel sum is calculated to find out the calibration factor (area per pixel); lastly, the object is extracted from background and the pixel sum of object is multiplied by the calibration factor to obtain its area. The proposed method was evaluated on the two sets of images: (1) rectangular object; and (2) circular object. The authors respond to the crucial requirement for accurate object measurement by presenting a systematic approach that includes image pre-processing, boundary detection, and calibration factor computation. The proposed methodology is rigorously validated through a series of empirical experiments, revealing its efficacy in accurately quantifying objects with an error of 0.02 cm2. This work constitutes a substantial contribution to the field of computer vision, offering a practical and versatile solution for object measurement tasks with broad applications.

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