Dr. Adrian Kaehler is an independant scientist, adviser, and start-up founder. His current research includes topics in deep learning, machine learning more generally, statistical modeling, and computer vision. Adrian received his Ph.D. in Theoretical Physics from Columbia university in 1998. Adrian has since held positions at Intel Corporation and the Stanford University AI Lab, is an Applied Invention Fellow, and was a member of the winning Stanley race team in the DARPA Grand Challenge. He is a co-founder of the Silicon Valley Deep Learning Group, and has a wide variety of published papers and patents in physics, electrical engineering, computer science, and robotics.
"...definitely a very useful and highly recommended introduction and reference.
--David Brenner, Computer Science House
"This book by two leading roboticists is much more than a programming guide for how to call functions in a programming library. You will enjoy the trip through practical application of Computer Vision and the overview of Machine Learning."
--Ira Laefsky, Amazon.com
"...a vital addition to the library of any computer vision practitioner."
--Trevor Darrell, Professor, EECS, UC Berkeley
"The book provides a clearly written introduction and tutorial that makes computer vision applications accessible to a range of new audiences."
--Ken Goldberg, Professor of Engineering, UC Berkeley and Vice-President of Technical Activities, IEEE Robotics and Automation Society
"This book's main job is to provide an introduction to OpenCV. It does that very well. It does something more: it reviews clearly and concisely many of the main concepts in machine vision. The book may be used to support an introductory machine vision course, picking up where the lectures left off and holding the students' hand in bringing
what they learned in class to life."
--Professor Pietro Perona, Professor of Electrical Engineering & Computation & Neural Systems, Director of the National Science Foundation Engineering Research Center in Neuromorphic Systems Engineering
"Learning OpenCV provides a conceptual framework for this well-maintained and optimized collection of software, and makes its components accessible through clear background explanations, a thorough yet simple description of the function interfaces, and well chosen, attractive examples. Much more than a user manual, this book would form an excellent basis for a hands-on, introductory course in computer vision."
--Carlo Tomasi, CS Prof. at Duke University
"This highly accessible book should be of great value to anyone interested in building and understanding computer vision applications. It fills an important gap between many more theoretically-oriented textbooks in the field of computer vision, by providing a highly accessible hands-on guide on how to build actual computer vision software. If you are like me, you will find it a pleasure to read, and full of insights. This is a highly practical book."
--Sebastian Thrun, Professor of Computer Science and Electrical Engineering at Stanford University
"Gary Bradski, the primary developer of OpenCV, and co-author Adrian Kaehler, have done an excellent job in writing this book. The chapters are written in a highly user-friendly fashion, with clear explanations and hands-on examples. The book can be used as both a reference book to the OpenCV library as well as a tutorial or text for many fundamental concepts in computer vision. This is a must-have book on any computer vision bookshelf. "
--L. Fei - Fei, Computer Science Dept., Princeton University
"Learning OpenCV will likely occupy a prominent spot on the bookshelf of almost anyone working in computer vision...As readers, we are fortunate to have this chance to learn about OpenCV directly from Gary Bradski, who was the driving force and prime developer behind OpenCV. With his coauthor, Adrian Kaehler, they provide a clear and well-thought-out overview of the entire OpenCV code base, with many simple introductory examples that make it easy to slowly gain expertise with this large library of software. "
--David Lowe, Professor, Computer Science, University of British Columbia
"Gary Bradksi has done the computer vision community a great service by organizing and championing the OpenCV library. This library is useful for practicioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised."
--Bill Freeman, Professor, EECS, MIT