Computational Geometry: Methods and Applications
by Jianer Chen
Number of pages: 227
In this book, we concentrate on four major directions in computational geometry: the construction of convex hulls, proximity problems, searching problems and intersection problems. Computational geometry is of practical importance because Euclidean space of two and three dimensions forms the arena in which real physical objects are arranged. A large number of applications areas such as pattern recognition, computer graphics, image processing, operations research, statistics, computer-aided design, robotics, etc., have been the incubation bed of the discipline since they provide inherently geo metric problems for which efficient algorithms have to be developed. A large number of manufacturing problems involve wire layout, facilities location, cutting-stock and related geometric optimization problems. Solving these efficiently on a high-speed computer requires the development of new geo metrical tools, as well as the application of fast-algorithm techniques, and is not simply a matter of translating well-known theorems into computer programs. From a theoretical standpoint, the complexity of geometric algo rithms is of interest because it sheds new light on the intrinsic difficulty of computation.
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by Steven M. LaValle - Cambridge University Press
Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this book tightly integrates a vast body of literature from several fields into a coherent source for reference in applications.
by Macneil Shonle, Matthew Wilson, Martin Krischik - Wikibooks
An accessible introduction into the design and analysis of efficient algorithms. It explains only the most basic techniques, and gives intuition for and an introduction to the rigorous mathematical methods needed to describe and analyze them.
by Anil K. Jain, Richard C. Dubes - Prentice Hall
The book is useful for scientists who gather data and seek tools for analyzing and interpreting data. It will be a reference for scientists in a variety of disciplines and can serve as a textbook for a graduate course in exploratory data analysis.
by Luc Devroye - Birkhauser
In these lecture notes, we attempt to explain the connection between the expected time of various bucket algorithms and the distribution of the data. The results are illustrated on standard searching, sorting and selection problems.