Methods for Image Threshold Determination

The following MATLAB programs demonstrate various image threshold determination techniques, including iterative thresholding, minimum within-class variance (Otsu's method), maximum entropy thresholding, and edge detection using MATLAB's built-in functions. These tested and executable implementations provide practical approaches for threshold selection and edge detection in image processing applications.

Key implementations include: - Iterative thresholding algorithm that repeatedly refines threshold values based on image histogram analysis - Otsu's method implementation that minimizes intra-class variance by calculating optimal separation between background and foreground - Maximum entropy thresholding that maximizes the entropy between two classes for optimal segmentation - Edge detection using MATLAB functions like edge() with various operators (Sobel, Canny, etc.)

All code includes proper histogram processing, threshold calculation routines, and visualization components for result verification. The implementations follow standard image processing workflows: loading images, converting to grayscale when necessary, applying threshold algorithms, and displaying binary results.