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Finding and recognizing objects in natural scenes: complementary computations in the dorsal and ventral visual

Edmund T Rolls1, Tristan J Webb2

  • 1Department of Computer Science, University of Warwick Coventry, UK ; Oxford Centre for Computational Neuroscience Oxford, UK.

Frontiers in Computational Neuroscience
|August 28, 2014
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Summary

This study models how the dorsal and ventral visual streams work together for object recognition. Combining saliency detection with a hierarchical model (VisNet) achieves 90% accuracy in complex scenes.

Keywords:
VisNetinferior temporal visual cortexinvarianceobject recognitionsaliencytrace learning rule

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Area of Science:

  • Neuroscience
  • Computer Vision
  • Computational Neuroscience

Background:

  • Object recognition in natural scenes involves eye movements (saccades) to bring targets within the receptive fields of neurons.
  • The dorsal and ventral visual streams play distinct but complementary roles in visual processing.

Purpose of the Study:

  • To model how the dorsal and ventral visual streams cooperate to enable object search and recognition.
  • To develop a computational model that integrates saliency-based eye movement control with hierarchical object recognition.

Main Methods:

  • Modeled dorsal stream saliency using graph-based visual saliency to guide eye fixations.
  • Developed a four-layer hierarchical model (VisNet) of the ventral visual stream for object recognition.
  • Trained VisNet using a synaptic modification rule with short-term memory for view and translation invariance.

Main Results:

  • The integrated model achieved approximately 90% correct object recognition for 4 objects across various views (135°) and positions in a scene.
  • The model demonstrated generalization within trained views and translations.
  • The approach highlights complementary computations between dorsal and ventral streams.

Conclusions:

  • The combined dorsal and ventral stream computations are crucial for locating and recognizing objects in complex natural environments.
  • The proposed model provides insights into the neural mechanisms underlying visual search and object recognition.