Segmentation of images through curve fitting analysis by modified Vandermonde matrix and modified Gram‐Schmidt method
Kuldip Acharya, Dibyendu Ghoshal, Bidyut Kumar Bhattacharyya · IET Image Processing · 2020
An algorithm is proposed for segmentation of digital images using the curve fitting method based on modified Vandermonde matrix and modified Gram‐Schmidt method. Modified Vandermonde matrix is applied to linearly smoothed histogram data to find the coefficients of a polynomial of a given degree for fitting the data in a least square sense. Modified Gram‐Schmidt method is applied to get polynomial coefficients to minimise the least square distance during the calculation. The threshold value is computed by locating the minimum number of pixels from the grey‐level value. Threshold value is used with that of the input image to generate an output segmented image. The output segmented image is further improved by applying morphological operation which has made the segmented edge lines and regions free from any extraneous pixels. The final segmented image has been found to have sharp edges and filled in a region within the image contours. The experimental results show that the proposed method has produced superior performance compared to other state‐of‐art algorithms. Further, it is observed from the results that the proposed algorithm takes less program execution time than others in vogue algorithms.