The main steps present in the figure are as follows: (1) A model receives an input image; (2) features of the image are extracted and organized in a hierarchical manner, with separate feature channels consisting of several levels based on the scale of processing (usually features are downscaled).

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Salience Models: A Computational Cognitive Neuroscience Review.

Received: 18 March 2019 Revised: 12 October 2019 Accepted: 22 October 2019 Published: 25 October 2019.

Many implementations of the model take into account other features like edges, motion, junctions, shades, etc.

The features are extracted to individual feature maps using parallel processing, which means that separate feature channels are analysed simultaneously in parallel; (3) the feature maps extracted at every level are merged into one general feature map organized per feature, with the most conspicuous locations reflected on the maps; and (4) the resulting feature maps are combined into a single map of conspicuity, which is the output of the model in the form of a so-called salience map.

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