Shape from Contour: Computation and Representation
Authors: James H Elder
Journal: Annual Review of Vision Science (2018)
Discipline: NEUROSCIENCE & BEHAVIOR
Keywords: contour; form; object; shape.
Affiliations: Centre for Vision Research, York University, Toronto, Ontario M3J 1P3, Canada; email: .

Abstract:  The human visual system reliably extracts shape information from complex natural scenes in spite of noise and fragmentation caused by clutter and occlusions. A fast, feedforward sweep through ventral stream involving mechanisms tuned for orientation, curvature, and local Gestalt principles produces partial shape representations sufficient for simpler discriminative tasks. More complete shape representations may involve recurrent processes that integrate local and global cues. While feedforward discriminative deep neural network models currently produce the best predictions of object selectivity in higher areas of the object pathway, a generative model may be required to account for all aspects of shape perception. Research suggests that a successful model will account for our acute sensitivity to four key perceptual dimensions of shape: topology, symmetry, composition, and deformation. 

Retracted: No

PubPeer comments:
  The leap from 2D to 3D object recognition is profound as a result of out-of-plane pose variation: As the object rotates in 3D, different parts of it are seen. Historically, two major theories for how the brain handles this problem have been advanced. In the 3D modelu2013based theory, the brain uses 2D features of the image projection of an object to estimate the 3D geometry and pose of the object, allowing comparison with stored 3D object models (e.g., Biederman 1987). In the alternative view-based theory (e.g., Bulthoff Edelman 1992), the brain maintains models for appearance of remembered objects over a sampling of 3D poses. The 2D image appearance of an object segmented from a new image can then be compared to stored 2D views to identify both the identity and pose of the new object. Despite the qualifier u201chistorically,u201d the two views described above treated as current. Both the description of the problem and the theories in play seem akin to vehicles missing the engine. The phrase u201cthe leap from 2D to 3D object recognitionu201d is leaping over the primary problem, which is the achievement of a 3D shape percept from the 2D retinal projection. The problem is not merely that as the object rotates different parts of it are seen; the problem is that 2D projection of the object in any orientation is potentially a projection of an infinite number of possible 3D object shapes. Shape is the most fundamental and diagnostic feature of objects. An object shape must be seen first in order to be recognized, in the event it has been seen before. Both of the u201ctheoriesu201d invoked give short shrift to this major, unsolved problem. To say that u201cthe brain uses 2D features of the image projection of an object to estimate the 3D geometry and pose of the objectu201d is not a theoretical proposal but a simple and rather uninformative statement of fact u2013 of necessity, perception of shape is based on the retinal projection. How this is achieved remains a deep mystery. Comparing the shapes currently perceived with shapes perceived in the past is a problem of memory not perception. But since perception comes first, the u201cmodel-basedu201d theory is a theory of neither. The u201cview-basedu201d theory similarly leaps blithely over the major problems to be solved as though they were minor bumps in the road: u201cthe brain maintains models for appearance of remembered objects over a sampling of 3D poses.u201d This shows a lack of understanding that we typically don t need to view an object from several viewpoints in order to grasp its shape. And the idea of collecting u201cposesu201d for a particular shape leaves open the question of how we infer that each pose is an aspect of the same object? Again, the theory appeals to memory processes which can t come into play until the prior problem of shape perception is solved. ,