We describe a template model for perception of edge blur and identify a crucial early nonlinearity in this process. The main principle is to spatially filter the edge image to produce a 'signature', and then find which of a set of templates best fits that signature. Psychophysical blur-matching data strongly support the use of a second-derivative signature, coupled to Gaussian first-derivative templates. The spatial scale of the best-fitting template signals the edge blur. This model predicts blur-matching data accurately for a wide variety of Gaussian and non-Gaussian edges, but it suffers a bias when edges of opposite sign come close together in sine-wave gratings and other periodic images. This anomaly suggests a second general principle: the region of an image that 'belongs' to a given edge should have a consistent sign or direction of luminance gradient. Segmentation of the gradient profile into regions of common sign is achieved by implementing the second-derivative 'signature' operator as two first-derivative operators separated by a half-wave rectifier. This multiscale system of nonlinear filters predicts perceived blur accurately for periodic and aperiodic waveforms. We also outline its extension to 2-D images and infer the 2-D shape of the receptive fields.
|Publication status||Unpublished - 2002|
|Event||25th European Conference on Visual Perception - Glasgow , United Kingdom|
Duration: 25 Aug 2002 → 29 Aug 2002
|Conference||25th European Conference on Visual Perception|
|Period||25/08/02 → 29/08/02|
Bibliographical noteAbstract published in ECVP 2002 Abstract Supplement, Perception, (August 2002, 1990) 13 (Supplement), p.54, 0301-0066.
- edge blur
- early nonlinearity
- spatially filter
- blur-matching data
- second-derivative signature
- Gaussian first-derivative templates
- Gaussian edges
- non-Gaussian edges
- receptive fields