|
Showing 1 - 2 of
2 matches in All Departments
This modern treatment of computer vision focuses on learning and
inference in probabilistic models as a unifying theme. It shows how
to use training data to learn the relationships between the
observed image data and the aspects of the world that we wish to
estimate, such as the 3D structure or the object class, and how to
exploit these relationships to make new inferences about the world
from new image data. 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 modify to build
useful vision systems. Primarily meant for advanced undergraduate
and graduate students, the detailed methodological presentation
will also be useful for practitioners of computer vision. * Covers
cutting-edge techniques, including graph cuts, machine learning and
multiple view geometry * A unified approach shows the common basis
for solutions of important computer vision problems, such as camera
calibration, face recognition and object tracking * More than 70
algorithms are described in sufficient detail to implement * More
than 350 full-color illustrations amplify the text * The treatment
is self-contained, including all of the background mathematics *
Additional resources at www.computervisionmodels.com
|
You may like...
Ghost Lover
Lisa Taddeo
Paperback
R470
R99
Discovery Miles 990
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.