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Related Concept Videos

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Related Experiment Video

Updated: May 20, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Learning view invariant recognition with partially occluded objects.

James M Tromans1, Irina Higgins, Simon M Stringer

  • 1Experimental Psychology, Oxford Foundation for Theoretical Neuroscience and Artificial Intelligence, University of Oxford Oxford, UK.

Frontiers in Computational Neuroscience
|August 1, 2012
PubMed
Summary
This summary is machine-generated.

This study shows how a neural network model (VisNet) can learn view-invariant object representations, even for occluded objects, using Continuous Transformation learning. The model successfully identifies individual objects from partial views.

Keywords:
continuous transformationinferior temporal cortexobject recognitionocclusion

Related Experiment Videos

Last Updated: May 20, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

  • Computational neuroscience
  • Computer vision
  • Machine learning

Background:

  • Developing artificial systems that can recognize objects from various viewpoints is a significant challenge.
  • Understanding how biological visual systems achieve view invariance is crucial for advancing AI.
  • Previous models often struggle with partially occluded objects during training.

Purpose of the Study:

  • To investigate the capability of a neural network model, VisNet, to form separate, view-invariant representations of multiple rotating objects.
  • To address the challenge of object recognition when objects are partially occluded during training.
  • To demonstrate how Continuous Transformation (CT) learning can facilitate the integration of partial views into a unified object representation.

Main Methods:

  • Utilizing VisNet, a neural network model simulating the ventral visual pathway.
  • Implementing Continuous Transformation (CT) learning, which leverages spatial similarity between successive object views.
  • Training the network with multiple rotating objects, where one object is consistently partially occluded.

Main Results:

  • VisNet successfully developed output layer cells that responded invariantly to specific objects across most or all views.
  • Each object, including the occluded one, was uniquely represented by a distinct subset of output cells.
  • The model demonstrated the ability to link separate partial views of the occluded object into a single, coherent representation.

Conclusions:

  • Continuous Transformation learning enables neural networks to achieve view-invariant object recognition, even with partial occlusion.
  • VisNet can form robust, individual object representations by integrating information from successive, spatially similar views.
  • This approach offers a potential mechanism for how biological systems might achieve object recognition under challenging visual conditions.