A gain-control disparity energy model for perceived depth from disparity

Area of Science:

• Vision science
• Computational neuroscience

Background:

• Luminance contrast significantly impacts object visibility and depth perception.
• Existing disparity models fail to explain the influence of luminance contrast on perceived depth.

Purpose of the Study:

• To develop a computational model of disparity processing that accounts for the effect of luminance contrast on perceived depth.
• To integrate contrast normalization into the disparity energy model.

Main Methods:

• Developed a model incorporating contrast normalization of neural responses into the disparity energy model.
• The model features disparity channels with varying selectivity, processing binocular images through receptive fields.
• A nonlinear contrast gain control mechanism was applied to the combined receptive field outputs.

Main Results:

• The model successfully accounts for the contrast dependence of perceived depth from binocular disparity.
• Perceived depth is determined by the weighted average of responses across all disparity channels.

Conclusions:

• The proposed model offers the first analytic explanation for how luminance contrast influences perceived depth from binocular disparity.
• This work advances our understanding of visual processing and depth perception mechanisms.