Selective Visual Attention: Computational Models and Applications

5.1 Attention of Population-based Inference

A biologically plausible top-down model was proposed in 2000 [11], and then enhanced in 2004 [28] and 2005 [12] by Hamker. It is a complete top-down computational model, since it concerns prior knowledge's memory, representation, learning and integration with observed data. Moreover, in this model all the computations are based on cell populations; that is, any kind of feature in each location is represented by a cell population. Each cell in the population has its preferred value as with simple cells in the brain. Regardless of the population, the flowchart of the top-down model is illustrated in Figure 5.1.

Figure 5.1 The flowchart of the population-based inference top-down model

The data flow between blocks of Figure 5.1 includes feed-forward from the left to the right, the feedback from the right to the left and some interconnects between blocks indicated by the arrows. In the feed-forward part, four feature channels such as intensity (I), red-green (RG), blue-yellow (BY) and orientation (θ) are computed from the input image by filtering and down-sampling to four pyramids (each pyramid includes eight feature maps, 1 . . . 8) as in the BS model mentioned in Chapter 3. The centre–surround contrast processing in the four pyramids generates their respective contrast maps. The above processing is shown in the leftmost rounded rectangle ...