Based on the highly successful 3-volume reference Handbook of Computer Vision and Applications, this concise edition covers in a single volume the entire spectrum of computer vision ranging form the imaging process to high-end algorithms and applications. This book consists of three parts, including an application gallery. Bridges the gap between theory and practical applications Covers modern concepts in computer vision as well as modern developments in imaging sensor technology Presents a unique interdisciplinary approach covering different areas of modern science
He is currently an Affiliate Professor at the University of Washington where he co-developed (with Steve Seitz) the widely adopted computer vision curriculum on which this book is based.
You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset.
Agarwala, 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., ...
This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision ...
The book familiarizes readers with fundamental concepts and issues related to computer vision and major approaches that address them.
This book constitutes the refereed proceedings of the 20th Iberoamerican Congress on Pattern Recognition, CIARP 2015, held in Montevideo, Uruguay, in November 2015.
You’ll also find this book useful if you’re a C++ programmer looking to extend your computer vision skillset by learning OpenCV.
Förstner, W. (1994) A framework for low level feature extraction, in Third European Conference on Computer Vision, Lecture Notes in Computer Science, vol. 801 (ed. J.O. Eklundh), Springer-Verlag, Berlin, pp. 383–394.
This book presents a collection of high-quality research by leading experts in computer vision and its applications.
What You Will Learn · Employ image processing, manipulation, and feature extraction techniques · Work with various deep learning algorithms for computer vision · Train, manage, and tune hyperparameters of CNNs and object detection models ...