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Journal Article

Distorted Low-Level Visual Features Affect Saliency-Based Visual Attention


Bahmani,  H
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bahmani, H., & Wahl, S. (2016). Distorted Low-Level Visual Features Affect Saliency-Based Visual Attention. Frontiers in Computational Neuroscience, 10: 124, pp. 1-4. doi:10.3389/fncom.2016.00124.

Cite as: https://hdl.handle.net/21.11116/0000-0000-7951-D
Image distortions can attract attention away from the natural scene saliency (Redi et al., 2011). Performance of viewers in visual search tasks and their fixation patterns are also affected by different types and amounts of distortions (Vu et al., 2008). In this paper, we have discussed the opinion that distortions could largely affect the performance of predictive models of visual attention, and simulated the effects of distorted low-level visual features on the saliency-based bottom-up visual attention. Saliency is a fast and pre-attentive mechanism for orienting visual attention to intrinsically important objects which pop-out more easily in a cluttered scene. Distortion of the low-level features that contribute to saliency may impair the readiness of the visual system in detection of salient objects, which may have major implications for critical situations like driving or locomotion. These distortions in natural life can be introduced by eye diseases such as cataract, or spectacles which may alter color perception (de Fez et al., 2002) or cause undesired optical effects like blurring, non-uniform magnification, and image displacement (Barbero and Portilla, 2016). The extent to which each of these distorted saliency features may affect the attentional performance is addressed in this paper by employing a biologically-inspired predictive model of visual attention. We briefly summarize the current standing of computational work on visual attention models in the following section and suggest a simple and influential model of saliency to examine the above hypothesis. Furthermore, we demonstrate in an example the hindered performance of the predictive saliency model on distorted images.