Model of spatial frequency column in primary vision system

In this paper, we propose a new model of spatial frequency column (SFC) in primary vision pathway based on multiresolution quasi-wavelet frequency plane sampling. The proposed model uses a set of parameter variable filters to sample frequency plane to distinguish different frequency components. The design of these filters is based on following criteria: 1) Channel filters should be well localized in both time and frequency domains. According to uncertainty principle, the Gaussian function is a best candidate. 2) The overlap between filters should be as less as possible to facilitate delimitation of distinctive frequency channels. 3) The filter bandwidth and its center frequency is a constant under same sampling resolution since vision system is more sensitive to higher frequency components. 4) The sampling density in frequency plane can be adaptively controlled, so that the obtained channels are not redundant and the details of signal are not lost, which means the sampling should be of multiresolution. When the insufficiency of sampling density at a point is detected, the sampling algorithm can adaptively increase the sampling density in the vicinity of that point through raising the resolution parameter. The sampling algorithm can be simply described as follows: 1) Define a lower resolution. 2) Use above criteria to yield N filters. 3) Convolute the image with the filters to produce N outputs. 4) Detect the sufficiency of sampling density at each filter center, if the sampling is too sparse then increase the resolution at that point. This is a recursive procedure. 5) At each pixel, take the maximum value of its frequency distribution set as its estimation value. Some experimental results show that the proposed model can partly imitate the SFC in the primary vision pathway.