Abstract
Observers make sense of scenes by parsing images on the retina into meaningful objects. This ability is retained for line drawings, demonstrating that critical information is concentrated at object boundaries. Information theoretic studies argue for further concentration at points of maximum curvature, or corners, on such boundaries [1]–[3] suggesting that the relative positions of such corners might be important in defining shape. In this study we use patterns subtly deformed from circular, by a sinusoidal modulation of radius, in order to measure threshold sensitivity to shape change. By examining the ability of observers to discriminate between patterns of different frequency and/or number of cycles of modulation in a 2x2 forced choice task we were able to show, psychophysically, that difference in a single cue, the periodicity of the corners (specifically the polar angle between two points of maximum curvature) was sufficient to allow discrimination of two patterns near their thresholds for detection. We conclude that patterns could be considered as labelled for this measure. These results suggest that a small number of such labels might be sufficient to identify an object.
Highlights
A central task of the visual system is to segment objects from the background of a scene and to identify them so that appropriate actions can be planned
Pair (a) comprises an RF3 and an RF6 pattern (A = 0.05 for all RF3 patterns and A = 0.0135 for RF6 patterns – these amplitudes are half those at which concave curvature features appear on the path and result in the deformation in examples of RF3 and RF6 patterns having approximately equal salience [22])
In this study we have shown that RF3 and RF6 patterns are perfectly discriminated
Summary
A central task of the visual system is to segment objects from the background of a scene and to identify them so that appropriate actions can be planned. The visual system could identify critical local properties and develop a parametric description of those local features, using variation of those parameters to discriminate between shapes and detect when collections of those features are present in order to recognise previously learnt shapes. Discrimination would be supported by changes in activation across the family of generic shape templates and recognition could be supported by particular patterns matching previously learnt object contours. A third alternative is to have very specific templates for particular objects such that activation of the template identifies the object This latter possibility has been discussed at length elsewhere [4,5] and has been rejected as a general solution because of the very substantial number of templates that would be required to account for size, viewpoint and orientation invariances for every recognised object
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