By Topic

Visual experience acquisition based on view angle estimation from 3D monocular image

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Hui Wei ; Department of Computer Science, Lab of Algorithm for Cognitive Model, Fudan University, Shanghai 200433, China

It has been proved that acquired training is important to the development of stereopsis experience. Month-old babies already have the initial experience of invariance recognition of 3D objects. There is a slight lack of precision in the interpretation of biological vision. However, the small cost and the fast speed in calculation meet the requirements of invariance recognition, the rich visual experience in which play an important role. But what is the experience, how to acquire and how to use, these problems have never been satisfactorily resolved. In this paper we simulate the learning of visual experience in children, and solve a view angle estimated problem by using self-organizing network, which make the hidden experience clarified. Compared to the Classic camera calibration, which a large number of parameters need to be estimated, this method needs only one image and does not aim to 3D reconstruction. By avoiding the complex calibration and registration process, an amount of computation has been reduced. Visual experiences are all obtained from the most ordinary examples, and the characterization based on the geometric feature. Therefore, this method has strong expansibility and good generalization ability.

Published in:

2008 International Conference on Machine Learning and Cybernetics  (Volume:7 )

Date of Conference:

12-15 July 2008