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Computational Intelligence and Neuroscience
Volume 2015, Article ID 708759, 9 pages
Research Article

2D Geometry Predicts Perceived Visual Curvature in Context-Free Viewing

ICube, UMR 7357, CNRS, Université de Strasbourg, 2 rue Boussingault, 67000 Strasbourg, France

Received 29 April 2015; Revised 10 July 2015; Accepted 21 July 2015

Academic Editor: Francesco Camastra

Copyright © 2015 Birgitta Dresp-Langley. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Planar geometry was exploited for the computation of symmetric visual curves in the image plane, with consistent variations in local parameters such as sagitta, chordlength, and the curves’ height-to-width ratio, an indicator of the visual area covered by the curve, also called aspect ratio. Image representations of single curves (no local image context) were presented to human observers to measure their visual sensation of curvature magnitude elicited by a given curve. Nonlinear regression analysis was performed on both the individual and the average data using two types of model: (1) a power function where (sensation) tends towards infinity as a function of (stimulus input), most frequently used to model sensory scaling data for sensory continua, and (2) an “exponential rise to maximum” function, which converges towards an asymptotically stable level of as a function of . Both models provide satisfactory fits to subjective curvature magnitude as a function of the height-to-width ratio of single curves. The findings are consistent with an in-built sensitivity of the human visual system to local curve geometry, a potentially essential ground condition for the perception of concave and convex objects in the real world.