Previous edition: Computer and machine vision / 4th ed. 2012.
Starting from classical linear approximation, this is a self-contained presentation of modern multivariate approximation theory that explores its connections with other areas... Læs mere
If you want a basic understanding of computer vision's underlying theory and algorithms, this... Læs mere
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. In particular, Bayesian methods have grown from a... Læs mere
This comprehensive guide to the restoration of images degraded by motion blur brings together a wide range of approaches to the problem, blending basic theory... Læs mere
This book provides an ideal reference to all the medical imaging researchers and professionals to explore their innovative methods and analysis on imaging technologies for better prospective of patient care.
Features: provides an introduction to the basic notation and mathematical concepts for describing an image and the key concepts for... Læs mere
Many industry experts consider unsupervised learning the next frontier in artificial... Læs mere
With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and... Læs mere
This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach.
Appropriate for upper-division undergraduate and graduate level courses in computer vision found in departments of computer science, computer engineering and electrical engineering, this book offers a treatment of modern computer vision methods.
Presents research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. The book highlights a wide range of topics such as video segmentation, object recognition, and 3D modelling.