Geometric Transformation

Geometric transformations, encompassing operations like rotation, scaling, and translation, are central to many fields, with current research focusing on improving the robustness and efficiency of systems under these transformations. This involves developing novel algorithms and model architectures, such as those based on piecewise linear approximations, deep learning (including convolutional neural networks), and Lagrangian systems, to address challenges in areas like image processing, neural network verification, and robotics. The ability to accurately model and predict geometric transformations has significant implications for various applications, including image watermarking, medical image analysis, and autonomous driving, by enhancing the reliability and performance of AI systems in real-world scenarios.