TY  - BOOK
AU  - Krig,Scott
ED  - SpringerLink (Online service)
TI  - Computer Vision Metrics: Textbook Edition 
SN  - 9783319337623
AV  - TA1637-1638 
U1  - 006.6 23
PY  - 2016///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Computer science
KW  - Text processing (Computer science)
KW  - Image processing
KW  - Computer Science
KW  - Image Processing and Computer Vision
KW  - Signal, Image and Speech Processing
KW  - Document Preparation and Text Processing
N1  - Image Capture and Representation -- Image Re-processing -- Global and Regional Features -- Local Feature Design Concepts -- Taxonomy of Feature Description Attributes -- Interest Point Detector and Feature Descriptor Survey -- Ground Truth Data, Content, Metrics, and Analysis -- Vision Pipeline and Optimizations -- Feature Learning Architecture Taxonomy and Neuroscience Background -- Feature Learning and Deep Learning Architecture Survey
N2  - Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods. To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized. The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCVand other imaging and deep learning tools
UR  - http://148.231.10.114:2048/login?url=http://dx.doi.org/10.1007/978-3-319-33762-3
ER  -