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Presents a comprehensive overview of topics of relevance to computer vision-related optimization
Facilitates understanding by focusing on the fundamental concepts, and providing clearly written and easy-to-follow explanations
Supplies detailed information on how to implement specific methods, such as pseudocode
Optimization plays an invaluable role in the exciting and rapidly developing field of computer vision, yet this importance is often overlooked in the literature.
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
This easy-to-follow and applications-focused book is an essential resource for researchers and practitioners seeking guidance on implementing specific methods in computer vision.
Marco Alexander Treiber is a software developer at ASM Assembly Systems, Munich, Germany, where he is Technical Lead in Image Processing for the Vision System of SiPlace placement machines, used in SMT assembly. Among his other publications is the successful Springer title An Introduction to Object Recognition.