By Topic

SOIM: a self-organizing invertible map with applications in active vision

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
$31 $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

2 Author(s)
Srinivasa, N. ; Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA ; Sharma, R.

We propose a novel neural network, called the self-organized invertible map (SOIM), that is capable of learning many-to-one functionals mappings in a self-organized and online fashion. The design and performance of the SOIM are highlighted by learning a many-to-one functional mapping that exists in active vision for spatial representation of three-dimensional point targets. The learned spatial representation is invariant to changing camera configurations. The SOIM also possesses an invertible property that can be exploited for active vision. An efficient and experimentally feasible method was devised for learning this representation on a real active vision system. The proof of convergence during learning as well as conditions for invariance of the learned spatial representation are derived and then experimentally verified using the active vision system. We also demonstrate various active vision applications that benefit from the properties of the mapping learned by SOIM

Published in:

Neural Networks, IEEE Transactions on  (Volume:8 ,  Issue: 3 )