There are numerous cues which influence human visual attention. Some of the cues cannot be explored by the conventional eye-tracking studies which makes use of a pictorial data presented to the observers on common displays. Depth perception occurs naturally in the real three-dimensional environment and, therefore, the depth cues are one of them. However, the eye-tracking studies in the real environment and their evaluation are complicated to carry out with a relevant number of participants while maintaining the laboratory conditions. We propose an experimental study methodology for exploring the depth perception tendencies during the free-viewing task on a widescreen display in a laboratory. This method is beyond the current hardware capabilities of the static eye-trackers mounted on the displays. Therefore, the eye-tracking glasses were used in the study to measure the attention data. We carried out the proposed study on a sample of 25 participants and created a novel dataset suitable for further visual attention research. The depth perception tendencies on a widescreen display were evaluated from the acquired data and the results were discussed in the context of the previous similar studies. Our results revealed some differences in the depth perception tendencies in comparison to the previous studies with the two-dimensional pictorial data and resembled some depth perception tendencies observed in the real environment.
Computational models predicting stimulus-driven human visual attention usually incorporate simple visual features, such as intensity, color and orientation. However, saliency of shapes and their contour segments influence attention too. Therefore, we built 30 own shape saliency models based on existing shape representation and matching techniques and compared them with 5 existing saliency methods. Since available fixation datasets were usually recorded on natural scenes where various factors of attention are present, we performed a novel eye-tracking experiment that primarily focuses on shape and contour saliency. Fixations from 47 participants who looked at silhouettes of abstract and realworld objects were used to evaluate the accuracy of proposed saliency models and investigate which shape properties are most attentive. The results showed that visual attention integrates local contour saliency, saliency of global shape features and shape dissimilarities. Fixation data also showed that intensity and orientation contrasts play an important role in shape perception. We found that humans tend to fixate first irregular geometrical shapes and objects whose similarity to a circle is different from other objects.
KEYWORDS: Visualization, Video, Glasses, Data modeling, RGB color model, Visual process modeling, 3D image processing, 3D modeling, Eye, Image segmentation
Most of the existing solutions predicting visual attention focus solely on referenced 2D images and disregard any depth information. This aspect has always represented a weak point since the depth is an inseparable part of the biological vision. This paper presents a novel method of saliency map generation based on results of our experiments with egocentric visual attention and investigation of its correlation with perceived depth. We propose a model to predict the attention using superpixel representation with an assumption that contrast objects are usually salient and have a sparser spatial distribution of superpixels than their background. To incorporate depth information into this model, we propose three different depth techniques. The evaluation is done on our new RGB-D dataset created by SMI eye-tracker glasses and KinectV2 device.
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