cogsci.ucsd.edu)
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1Department of Cognitive Science, UC San Diego 2Department of Electrical and Computer Engineering, UC San Diego |
The perceived brightness of a surface depends on the brightness of
neighboring surfaces. Here, we describe a new low-level computational model of
this effect. We extend the ODOG model (Blakeslee & McCourt, 1999), which
combines two simple mechanisms. First, the input is filtered by multiscale
oriented difference of Gaussian filters. Second, global response normalization
equalizes the amount of energy at each orientation across the entire visual
field.
In this work we extended the ODOG model with a more neurally plausible
normalization step. The normalization step in the ODOG model is necessary to
account for a family of illusions known as White’s effect, which are often
characterized by a highly non-uniform distribution of energy at different
orientations. ODOG fails on variations of White’s effect that have equal
energy across orientations when integrated over the entire image, suggesting a
more localized normalization scheme is necessary. A local mechanism also has the
advantage of being more plausible for implementation in early visual areas, such
as V1, because it only requires short-distance connections between neurons.
In our new model, Frequency-specific Locally-normalized ODOG (FLODOG), energy
normalization is computed locally, both in terms of spatial location and spatial
frequency. We filter the image into 6 different orientations and 7 scales. Each
filter response is normalized by a weighted sum that includes itself and also
filter responses for nearby spatial frequencies of the same orientation. This
normalization occurs within a local window, the size of which scales with the
spatial extent of the filter being normalized.
The FLODOG model successfully accounts for most of the illusions for which ODOG
makes correct predictions. In addition, it correctly predicts many variants of
White’s illusion that ODOG cannot.
poster (476K).
(c) 2007 Alan Robinson (robinsoncogsci.ucsd.edu)