Computational Vision and Mathematical Structures plus AI Expansion
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Computational vision advances through algorithms, image processing, neural networks, and mathematical structures. Algorithms extract information, while image processing handles diverse representations. Neural networks, like autoencoders, model complex visual relationships. Mathematics, especially graph theory and matrix operations, provides structured analysis. Oscillatory systems and network dynamics explain temporal processing and connectivity. Physics-inspired concepts enhance spatial understanding. Integrating these fields improves visual perception and analysis, leading to sophisticated vision systems.