Search results for "Divisive normalization"
showing 3 items of 3 documents
Appropriate kernels for Divisive Normalization explained by Wilson-Cowan equations
2018
The interaction between wavelet-like sensors in Divisive Normalization is classically described through Gaussian kernels that decay with spatial distance, angular distance and frequency distance. However, simultaneous explanation of (a) distortion perception in natural image databases and (b) contrast perception of artificial stimuli requires very specific modifications in classical Divisive Normalization. First, the wavelet response has to be high-pass filtered before the Gaussian interaction is applied. Then, distinct weights per subband are also required after the Gaussian interaction. In summary, the classical Gaussian kernel has to be left- and right-multiplied by two extra diagonal ma…
Visual information flow in Wilson-Cowan networks.
2020
In this paper, we study the communication efficiency of a psychophysically tuned cascade of Wilson-Cowan and divisive normalization layers that simulate the retina-V1 pathway. This is the first analysis of Wilson-Cowan networks in terms of multivariate total correlation. The parameters of the cortical model have been derived through the relation between the steady state of the Wilson-Cowan model and the divisive normalization model. The communication efficiency has been analyzed in two ways: First, we provide an analytical expression for the reduction of the total correlation among the responses of a V1-like population after the application of the Wilson-Cowan interaction. Second, we empiri…
In praise of artifice reloaded: Caution with natural image databases in modeling vision
2019
Subjective image quality databases are a major source of raw data on how the visual system works in naturalistic environments. These databases describe the sensitivity of many observers to a wide range of distortions of different nature and intensity seen on top of a variety of natural images. Data of this kind seems to open a number of possibilities for the vision scientist to check the models in realistic scenarios. However, while these natural databases are great benchmarks for models developed in some other way (e.g., by using the well-controlled artificial stimuli of traditional psychophysics), they should be carefully used when trying to fit vision models. Given the high dimensionalit…