Minimal Problem
Minimal problems in various fields, from computer vision to process mining, focus on finding solutions using the minimum necessary data, improving efficiency and robustness. Current research emphasizes developing efficient algorithms, often leveraging techniques like sparse resultants, the Implicit Function Theorem, and iterative refinement, to solve these minimal problems, particularly in areas like pose estimation and robust model fitting. These advancements lead to faster and more stable solutions for challenging computational tasks, impacting fields requiring real-time performance or dealing with noisy data, such as robotics, 3D reconstruction, and process analysis.
Papers
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