Extraction of Multiple Parameter Features for Histogram Analysis

This article presents a MATLAB program designed to extract multiple parameter features required for comprehensive histogram analysis. The computed parameters include mean (measuring central tendency), variance (quantifying data dispersion), skewness (assessing distribution asymmetry), kurtosis (evaluating tail heaviness), energy (representing signal strength), and entropy (measuring information content). These features enable deeper insights into image or signal properties, facilitating more thorough understanding of research subjects. The implementation leverages MATLAB's statistical toolbox functions like mean(), var(), skewness(), and kurtosis(), while energy and entropy are calculated through custom algorithms involving pixel intensity summation and probability distribution computations. Through this program, researchers can investigate underlying histogram mechanisms and perform more accurate data analysis for applications in image processing, signal processing, and related fields, thereby expanding potential research domains.