Explore MATLAB source code curated for "分割结果" with clean implementations, documentation, and examples.
In fingerprint feature extraction, image segmentation is performed by combining directional patterns with local grayscale variance, followed by threshold segmentation to eliminate edge effects and achieve optimal segmentation results. This method leverages orientation estimation and statistical texture analysis for improved feature localization.
A color image segmentation algorithm that first performs coarse segmentation using histogram analysis, followed by refined segmentation using k-means clustering to obtain final segmentation results. The implementation involves histogram-based color distribution analysis and iterative k-means optimization for precise region identification.
Implementation of a color image segmentation algorithm in MATLAB based on K-means clustering principles, using straightforward programming statements for clear understanding and easy adaptation. This implementation can be directly applied to color cell image segmentation with relatively accurate results.
Load a color image and execute the Untitled.m script: first interactively select a target color region for segmentation using the mouse. After selection, press the ENTER key to display the segmentation results.
Implementation of image segmentation based on Otsu's method and iterative algorithms, achieving superior segmentation results with robust threshold optimization techniques