Computational Geometry With Independent And Dependent Uncertainties

This book offers a thorough examination of geometric uncertainty through the Linear Parametric Geometric Uncertainty Model, providing valuable insights for various fields.

The book Computational Geometry With Independent And Dependent Uncertainties presents a detailed exploration of a novel parametric model and algorithmic theory designed to represent geometric entities with dependent uncertainties. This theory, known as the Linear Parametric Geometric Uncertainty Model (LPGUM), offers an expressive and computationally efficient framework. It enables a systematic study of geometric uncertainty and its related algorithms, which is crucial in the field of computer geometry.

The significance of Computational Geometry With Independent And Dependent Uncertainties extends beyond theoretical applications. Geometric uncertainty is a prevalent issue in various domains such as mechanical CAD/CAM, robotics, computer vision, and wireless networks. Traditional geometric models often assume exactness, neglecting the inaccuracies that can arise in real-world applications. This book addresses that gap by providing a robust framework to account for uncertainties, enhancing the reliability of geometric computations.

This self-contained monograph serves as an invaluable resource for academics, researchers, and practitioners across multiple disciplines, including computer science, robotics, and mechanical engineering. It not only elaborates on the theoretical foundations of the LPGUM but also illustrates its practical implications, making it a useful reference text for those looking to deepen their understanding of geometric uncertainty and its applications.