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      Why vision is not both hierarchical and feedforward

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      Author
      Herzog, M.
      Clarke, Aaron
      Date
      2014
      Source Title
      Frontiers in Computational Neuroscience
      Print ISSN
      1662-5188
      Publisher
      Frontiers
      Volume
      8
      Pages
      135
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      In classical models of object recognition, first, basic features (e.g., edges and lines) are analyzed by independent filters that mimic the receptive field profiles of V1 neurons. In a feedforward fashion, the outputs of these filters are fed to filters at the next processing stage, pooling information across several filters from the previous level, and so forth at subsequent processing stages. Low-level processing determines high-level processing. Information lost on lower stages is irretrievably lost. Models of this type have proven to be very successful in many fields of vision, but have failed to explain object recognition in general. Here, we present experiments that, first, show that, similar to demonstrations from the Gestaltists, figural aspects determine low-level processing (as much as the other way around). Second, performance on a single element depends on all the other elements in the visual scene. Small changes in the overall configuration can lead to large changes in performance. Third, grouping of elements is key. Only if we know how elements group across the entire visual field, we can determine performance on individual elements, i.e., challenging the classical stereotypical filtering approach, which is at the very heart of most vision models.
      Keywords
      Feedback
      Object recognition
      Crowding
      Verniers
      Gestalt
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      http://hdl.handle.net/11693/53576
      Published Version (Please cite this version)
      https://doi.org/10.3389/fncom.2014.00135
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