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Learning view invariant recognition with partially occluded objects

Authors
Journal
Frontiers in Computational Neuroscience
1662-5188
Publisher
Frontiers Media SA
Publication Date
Volume
6
Identifiers
DOI: 10.3389/fncom.2012.00048
Source
Legacy
Keywords
  • Neuroscience
  • Original Research Article
Disciplines
  • Computer Science

Abstract

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.

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