Optimal Threshold Selection for Road Image Segmentation Using Genetic Algorithm

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Resource Overview

This implementation utilizes genetic algorithms to determine the optimal threshold for road image segmentation, employing Otsu's maximum inter-class variance method as the selection criterion, with program execution achieving highly satisfactory segmentation results through evolutionary optimization.

Detailed Documentation

Using genetic algorithms to select optimal thresholds for road image segmentation proves to be an effective methodology. The implementation leverages Otsu's method – a maximum inter-class variance approach – for threshold determination. The genetic algorithm operates through fitness evaluation, chromosome crossover, and mutation operations to evolve toward optimal solutions. Key algorithmic components include population initialization with random threshold values, fitness calculation based on inter-class variance maximization, and elitism selection to preserve best-performing thresholds. Through iterative generations, the program converges to threshold values that produce exceptionally clean segmentation results, effectively separating road regions from background elements with minimized classification error.

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