Stereo Saliency Map Considering Affective Factors in a Dynamic Environment

We propose a new integrated saliency map model, which reflects more human-like visual attention mechanism. The proposed model considers not only the binocular stereopsis to construct a final attention area so that the closer attention area can be easily made to pop-out as in human binocular vision, based on the single eye alignment hypothesis, but also both static and dynamic features of an input scene. Moreover, the proposed saliency map model includes an affective computing process to skip an unwanted area and/or to pay attention to a desired area, mimicking the pulvinar's function in the human preference and refusal mechanism in subsequent visual search processes. In addition, we show the effectiveness of using the symmetry feature implemented by a neural network and independent component analysis (ICA) filter to construct more object preferable attention model. The experimental results show that the proposed model can generate more plausible scan paths for natural input scenes.