Extreme Value Theory-Based Methods for Visual Recognition (Synthesis Lectures on Computer Vision)

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(as of Dec 26,2024 17:09:26 UTC – Details)

Publisher ‏ : ‎ Springer; 1st edition (February 15, 2017)
Language ‏ : ‎ English
Paperback ‏ : ‎ 132 pages
ISBN-10 ‏ : ‎ 3031006895
ISBN-13 ‏ : ‎ 978-3031006890
Item Weight ‏ : ‎ 8.5 ounces
Dimensions ‏ : ‎ 7.52 x 0.3 x 9.25 inches

Extreme Value Theory-Based Methods for Visual Recognition (Synthesis Lectures on Computer Vision)

Visual recognition is a crucial task in computer vision, with applications ranging from surveillance and security to autonomous driving and medical imaging. Traditional methods for visual recognition often struggle to handle extreme cases, such as rare events or outliers in the data.

This is where Extreme Value Theory (EVT) comes in. EVT is a branch of statistics that focuses on modeling the tail behavior of extreme events in a dataset. By leveraging EVT-based methods, researchers can better capture and analyze extreme cases in visual recognition tasks, leading to more robust and reliable systems.

In this Synthesis Lectures on Computer Vision, experts in the field explore the application of EVT-based methods to visual recognition tasks. They discuss the theoretical foundations of EVT, its relevance to computer vision, and practical implementation strategies for using EVT in visual recognition systems.

Whether you’re a researcher, practitioner, or student in the field of computer vision, this book offers valuable insights into how EVT can enhance the performance of visual recognition systems. Stay ahead of the curve and dive into the world of Extreme Value Theory-based methods for visual recognition today.
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