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Computer vision metrics = survey, ta...

Krig, Scott.

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Computer vision metrics = survey, taxonomy, and analysis of computer vision, visual neuroscience, and visual AI /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computer vision metrics/ by Scott Krig.
Reminder of title:	survey, taxonomy, and analysis of computer vision, visual neuroscience, and visual AI /
Author:	Krig, Scott.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxvi, 790 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Computer vision. -
Online resource:	https://doi.org/10.1007/978-981-99-3393-8
ISBN:	9789819933938

Computer vision metrics = survey, taxonomy, and analysis of computer vision, visual neuroscience, and visual AI /
Krig, Scott.

Computer vision metricssurvey, taxonomy, and analysis of computer vision, visual neuroscience, and visual AI /[electronic resource] :by Scott Krig. - Second edition. - Singapore :Springer Nature Singapore :2025. - xxvi, 790 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789819933938

Standard No.: 10.1007/978-981-99-3393-8doiSubjects--Topical Terms:

540671
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Computer vision metrics = survey, taxonomy, and analysis of computer vision, visual neuroscience, and visual AI /
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505 0 $a 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.
520 $a 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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