Affordable Access

Publisher Website

Learning view invariant recognition with partially occluded objects

Frontiers in Computational Neuroscience
Frontiers Media SA
Publication Date
DOI: 10.3389/fncom.2012.00048
  • Neuroscience
  • Original Research Article
  • Computer Science


This paper investigates how a neural network model of the ventral visual pathway, VisNet, can form separate view invariant representations of a number of objects seen rotating together. In particular, in the current work one of the rotating objects is always partially occluded by the other objects present during training. A key challenge for the model is to link together the separate partial views of the occluded object into a single view invariant representation of that object. We show how this can be achieved by Continuous Transformation (CT) learning, which relies on spatial similarity between successive views of each object. After training, the network had developed cells in the output layer which had learned to respond invariantly to particular objects over most or all views, with each cell responding to only one object. All objects, including the partially occluded object, were individually represented by a unique subset of output cells.

There are no comments yet on this publication. Be the first to share your thoughts.


Seen <100 times

More articles like this

Learning view invariant recognition with partially...

on Frontiers in Computational Neu... Jan 01, 2012

Recognition of partially occluded 3-D objects by d...

on Pattern Recognition Letters Jan 01, 1988

Recognition and positioning of partially occluded...

on Pattern Recognition Letters Jan 01, 1991

Recognition of partially occluded objects by corre...

on Optics Communications Jan 01, 1994
More articles like this..