Physic Based Vision

Physics-based vision integrates physical principles into computer vision models to improve accuracy, robustness, and data efficiency. Current research focuses on leveraging deep learning alongside differentiable physics simulations and physics-informed neural networks for tasks like image segmentation, motion capture, and fluid simulation, often employing novel algorithms like Jacobian-scaled K-means clustering or physics-inspired convolutional filters. This interdisciplinary approach enhances the interpretability and generalizability of computer vision systems, leading to advancements in areas such as medical imaging, weather forecasting, and robotics.