The importance of intermediate representations for the modeling of 2D shape detection: Endstopping and curvature tuned computations

Computational models of visual processes with biological inspiration - and even biological realism - are currently of great interest in the computer vision community. This paper provides a biologically plausible model of 2D shape which incorporates intermediate layers of visual representation that have not previously been fully explored. We propose that endstopping and curvature cells are of great importance for shape selectivity and show how their combination can lead to shape selective neurons. This shape representation model provides a highly accurate fit with neural data from and provides comparable results with real-world images to current computer vision systems. The conclusion is that such intermediate representations may no longer require a learning approach as a bridge between early representations based on Gabor or Difference of Gaussian filters (that are not learned since they are well-understood) and later representations closer to object representations that still can benefit from a learning methodology.