Optimization for Computer Vision An Introduction to Core Concepts and Methods
“Optimization for Computer Vision: An Introduction to Core Concepts and Methods” by Marco Alexander Treiber is a practical text published by Springer London on July 24, 2013. This edition spans 257 pages and is presented in English. The book offers a broad introduction to the optimization methods specifically utilized in the field of computer vision, aiming to enhance understanding through clear explanations and visual aids.
Readers will find a comprehensive overview of various optimization techniques, ranging from classical iterative multidimensional methods to advanced topics such as graph cuts and GPU-suited total variation-based optimization. The text includes flow charts and pseudocode implementations that provide deeper insights into the operational aspects of these methods. Additionally, it features examples drawn from significant applications in computer vision, making it a valuable resource for those interested in the intersection of computers, artificial intelligence, and mathematics.
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This practical and authoritative text/reference presents a broad introduction to the optimization methods used specifically in computer vision. In order to facilitate understanding, the presentation of the methods is supplemented by simple flow charts, followed by pseudocode implementations that reveal deeper insights into their mode of operation. These discussions are further supported by examples taken from important applications in computer vision. Topics and features: provides a comprehensive overview of computer vision-related optimization; covers a range of techniques from classical iterative multidimensional optimization to cutting-edge topics of graph cuts and GPU-suited total variation-based optimization; describes in detail the optimization methods employed in computer vision applications; illuminates key concepts with clearly written and step-by-step explanations; presents detailed information on implementation, including pseudocode for most methods.
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