Otsu's Maximum Inter-class Variance Method for Image Segmentation using MATLAB

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Resource Overview

This algorithm implements Otsu's maximum inter-class variance method, utilizing MATLAB for effective image segmentation with code implementation details

Detailed Documentation

This algorithm is based on Otsu's maximum inter-class variance method, which employs MATLAB for image segmentation. The method effectively partitions images into distinct regions for subsequent analysis and processing. The core algorithm calculates the optimal threshold by maximizing the variance between different classes of pixels. In MATLAB implementation, key functions like graythresh automatically compute the optimal threshold value, while im2bw (or imbinarize in newer versions) applies the threshold to create a binary image. The algorithm works by iterating through all possible threshold values, computing the inter-class variance for each, and selecting the threshold that yields maximum separation between foreground and background pixel distributions.

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