Visual cortical processing—From image to object representation

Research output: Contribution to journalReview articlepeer-review


Image understanding is often conceived as a hierarchical process with many levels, where complexity and invariance of object representation gradually increase with level in the hierarchy. In contrast, neurophysiological studies have shown that figure-ground organization and border ownership coding, which imply understanding of the object structure of an image, occur at levels as low as V1 and V2 of the visual cortex. This cannot be the result of back-projections from object recognition centers because border-ownership signals appear well-before shape selective responses emerge in inferotemporal cortex. Ultra-fast border-ownership signals have been found not only for simple figure displays, but also for complex natural scenes. In this paper I review neurophysiological evidence for the hypothesis that the brain uses dedicated grouping mechanisms early on to link elementary features to larger entities we might call “proto-objects”, a process that is pre-attentive and does not rely on object recognition. The proto-object structures enable the system to individuate objects and provide permanence, to track moving objects and cope with the displacements caused by eye movements, and to select one object out of many and scrutinize the selected object. I sketch a novel experimental paradigm for identifying grouping circuits, describe a first application targeting area V4, which yielded negative results, and suggest targets for future applications of this paradigm.

Original languageEnglish (US)
Article number1136987
JournalFrontiers in Computer Science
StatePublished - 2023


  • computational model
  • figure ground organization
  • neural mechanism
  • object individuation
  • object permanence
  • selective attention
  • spiking synchrony
  • visual cortex

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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