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    Computer Vision Metrics: Survey, Taxonomy, and Analysis of Computer Vision, Visual Neuroscience, and Visual AI

    Computer Vision Metrics by Krig, Scott;

    Survey, Taxonomy, and Analysis of Computer Vision, Visual Neuroscience, and Visual AI

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      • Publisher's listprice EUR 117.69
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        49 924 Ft (47 546 Ft + 5% VAT)
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    49 924 Ft

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    Product details:

    • Edition number Second Edition 2025
    • Publisher Springer
    • Date of Publication 18 May 2025
    • Number of Volumes 1 pieces, Book

    • ISBN 9789819933921
    • Binding Hardback
    • No. of pages790 pages
    • Size 254x178 mm
    • Language English
    • Illustrations 1 Illustrations, black & white
    • 700

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    Short description:

    This 2nd Edition, based on the successful 2016 textbook, has been updated and expanded to cover 3rd generation Computer Vision and AI as it supersedes historical visual computing methods, providing a comprehensive survey of essential topics and methods in Computer Vision. With over 1,200 essential references, as well as chapter-by-chapter learning assignments, the book offers a valuable resource for students, researchers, scientists and engineers, helping them dig deeper into core computer vision and foundational visual computing and neuroscience topics. 

    As before, a historical survey of advances in Computer Vision is provided, updated to reflect the latest methods such as Vision Transformers, attention models, alternative features such as Fourier neurons and Binary neurons, hybrid DNN architectures, self-supervised and enhanced learning models, Associative Multimodal Learning, Continuous Learning, View Synthesis, intelligent Scientific Imaging, and advances in training protocols. Updates have also been added for 2d/3d cameras, software libraries and open source resources, computer vision cloud services, and vision/AI hardware accelerators. Discussion and analysis are provided to uncover intuition and delve into the essence of key advancements, applied and forward-looking topics. 



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    Long description:

    This 2nd Edition, based on the successful 2016 textbook, has been updated and expanded to cover 3rd generation Computer Vision and AI as it supersedes historical visual computing methods, providing a comprehensive survey of essential topics and methods in Computer Vision. With over 1,200 essential references, as well as chapter-by-chapter learning assignments, the book offers a valuable resource for students, researchers, scientists and engineers, helping them dig deeper into core computer vision and foundational visual computing and neuroscience topics. 

    As before, a historical survey of advances in Computer Vision is provided, updated to reflect the latest methods such as Vision Transformers, attention models, alternative features such as Fourier neurons and Binary neurons, hybrid DNN architectures, self-supervised and enhanced learning models, Associative Multimodal Learning, Continuous Learning, View Synthesis, intelligent Scientific Imaging, andadvances in training protocols. Updates have also been added for 2d/3d cameras, software libraries and open source resources, computer vision cloud services, and vision/AI hardware accelerators. Discussion and analysis are provided to uncover intuition and delve into the essence of key advancements, applied and forward-looking topics. 


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    Table of Contents:

    Chapter 1. 2D/3D Image Capture and Representation.- Chapter 2. Image Pre-Processing Taxonomy, Colorimetry.- Chapter 3. Global and Regional Feature Descriptors.- Chapter 4. Local Feature Descriptors.- Chapter 5. Feature Descriptor Attribute Taxonomy.- Chapter 6. Feature Detector and Descriptor Survey.- Chapter 7. Ground Truth Data Topics, Benchmarks, Analysis.- Chapter 8. Vision Pipelines and HW/SW Optimizations.- Chapter 9. Feature Learning Taxonomy and Neuroscience Background.-Chapter 10. Feature Learning and Deep Learning Survey.- Chapter 11. Attention, Transformers, Hybrids, DDN?s.- Chapter 12. Applied And Future Visual Computing Topics.






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