Camera Self-Calibration Parameter Calculation Using Genetic Algorithm

This work implements camera self-calibration parameter calculation using genetic algorithms, with detailed MATLAB programs included in the research paper for practical implementation.

Genetic Algorithm (GA) is an optimization technique that mimics natural evolutionary processes to search for optimal solutions. In camera self-calibration, GA automatically adjusts intrinsic parameters (focal length, principal point, skew coefficients) and extrinsic parameters (rotation and translation matrices) to enhance calibration accuracy and robustness. The MATLAB implementation visualizes this optimization process through iterative population evolution, featuring functions for chromosome encoding (parameter representation), fitness evaluation (reprojection error calculation), and genetic operations (selection, crossover, mutation).

Alternative self-calibration methods serve as comparative benchmarks, including template-based approaches using known calibration patterns and feature-point methods leveraging natural image features. These comparisons objectively evaluate GA's performance in terms of convergence speed, parameter accuracy, and noise resistance.

The comprehensive approach combining genetic algorithm theory with executable MATLAB code provides readers with both theoretical understanding and practical implementation capabilities, enabling deeper insight into camera self-calibration methodologies and reproducible experimental validation.