2D Histogram Visualization Using MATLAB

Creating 2D histograms of images using MATLAB is a standard approach for pixel intensity analysis and visualization. The implementation typically begins by loading the target image and converting it to grayscale format using functions like rgb2gray() for color images. The core histogram computation employs MATLAB's imhist() function, which calculates the frequency distribution of pixel intensities across 256 bins by default. For visualization, the bar() function is commonly used to plot the histogram data, where the x-axis represents intensity values (0-255) and the y-axis shows frequency counts. To enhance analytical capabilities, statistical measures such as mean intensity (mean2()) and variance (var()) can be calculated and annotated on the plot using text() or annotation() functions. Advanced implementations may include histogram equalization using histeq() for contrast enhancement, or customized bin sizes for specific analysis needs. The resulting histogram provides crucial insights into image characteristics including contrast distribution, brightness levels, and potential issues like underexposure or overexposure. Further analysis can involve comparing multiple histograms, calculating cumulative distribution functions, or applying thresholding techniques based on histogram valleys and peaks. This comprehensive approach enables deep understanding of image properties and supports various computer vision and image processing applications.