Explore MATLAB source code curated for "最大类间方差阈值" with clean implementations, documentation, and examples.
Tissue-Based Particle Swarm Optimization (T-PSO) for Maximum Between-Class Variance (Otsu) Threshold Image Segmentation with Implementation Details
Utilizing gray-scale integral projection for direct face detection and eye localization is a common algorithm, but its direct application can be affected by background noise and feature variations, resulting in lower recognition accuracy. We propose a detection and localization algorithm combining Otsu's thresholding method (maximum inter-class variance) and region dilation. The algorithm first calculates the Otsu threshold to binarize the gray-scale image and detect face regions, then performs dilation and connectivity processing on connected components within the face area to precisely locate eye coordinates. Experimental results demonstrate that this algorithm is reliable with improved recognition performance. Key implementation aspects include adaptive threshold calculation using Otsu's method and morphological operations for feature enhancement.
This approach combines traditional Otsu's thresholding method with modern genetic algorithm optimization to enhance image segmentation performance. When applied to road segmentation in intelligent transportation systems, it achieves improved segmentation accuracy while significantly accelerating computational efficiency to meet real-time processing requirements. The implementation involves optimizing threshold selection through genetic operators like crossover and mutation.