Computer Vision: Principles, Algorithms, Applications, Learning
- ISBN-10
- 012809284X
- ISBN-13
- 9780128092842
- Series
- Computer Vision
- Category
- Computers
- Pages
- 900
- Language
- English
- Published
- 2017-10
- Publisher
- Academic Press
- Author
- E. R. Davies
Description
Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/ Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition. A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application. In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics. Examples and applications-including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians-give the 'ins and outs' of developing real-world vision systems, showing the realities of practical implementation. Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples. The 'recent developments' sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject. Tailored programming examples-code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)
Get the book
Other editions
-
- 2017-11-15
- 900 pages
- Ebook
- Academic Press
-
- 2018-02-02
- 2451 pages
- Ebook
- IGI Global
-
- 2017-12-07
- 729 pages
- Ebook
- Springer
Similar books
-
Computer Vision: Models, Learning, and Inference
By Simon J. D. PrinceA modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.
-
Computer Vision: Algorithms and Applications
By Richard SzeliskiAgarwala, A., Agrawala, M., Cohen, M., Salesin, D., and Szeliski, R. (2006). Photographing long scenes with multi-viewpoint panoramas. ACM Transactions on Graphics (Proc. SIGGRAPH 2006), 25(3):853–861. Agarwala, A., Dontcheva, M., ...
-
Concise Computer Vision: An Introduction into Theory and Algorithms
By Reinhard KletteConcise Computer Vision provides an accessible general introduction to the essential topics in computer vision, highlighting the role of important algorithms and mathematical concepts.
-
Computer Vision for X-Ray Testing: Imaging, Systems, Image Databases, and Algorithms
By Domingo MeryTopics and features: Introduces the mathematical background for monocular and multiple view geometry, which is commonly used in X-ray computer vision systems Describes the main techniques for image processing used in X-ray testing, ...
-
Computer Vision Metrics: Survey, Taxonomy, and Analysis
By Scott KrigThis book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance ...
-
Programming Computer Vision with Python: Tools and algorithms for analyzing images
By Jan Erik SolemThis book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills.
-
Advanced Methods and Deep Learning in Computer Vision
By Matthew Turk, E. R. DaviesThis book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate ...
-
Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence
By Taylor & Francis GroupThis book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.
-
Multiple View Geometry in Computer Vision
By Andrew Zisserman, Richard Hartley[Fitzgibbon-98b| A. W. Fitzgibbon, G. Cross, and A. Zisserman. Automatic 3D model construction for turn-table sequences. In R. Koch and L. Van Gool. editors, 3D Structure from Multiple Images of Large-Scale Environments, ...
-
Practical Machine Learning for Computer Vision
By Valliappa Lakshmanan, Martin Görner, Ryan GillardThis practical book shows you how to employ machine learning models to extract information from images.