Visual Attention and Applications in Multimedia Technologies

Evaluation of Gaze Tracking Calibration for Longitudinal Biomedical Imaging Studies

Gaze tracking is a promising technology for studying the visual perception of clinicians during image-based medical exams. It could be used in longitudinal studies to analyze their perceptive process, explore human-machine interactions, and develop innovative computer-aided imaging systems. However, using a remote eye tracker in an unconstrained environment and over time periods of weeks requires a certain guarantee of performance to ensure that collected gaze data are fit for purpose. We report the results of evaluating eye tracking calibration for longitudinal studies. First, we tested the performance of an eye tracker on a cohort of 13 users over a period of one month. For each participant, the eye tracker was calibrated during the first session. The participants were asked to sit in front of a monitor equipped with the eye tracker, but their position was not constrained. Second, we tested the performance of the eye tracker on sonographers positioned in front of a cart-based ultrasound scanner. Experimental results show a decrease of accuracy between calibration and later testing of 0.30° and a further degradation over time at a rate of 0.13°. month^-1. The overall median accuracy was 1.00° (50.9 pixels) and the overall median precision was 0.16° (8.3 pixels). The results from the ultrasonography setting show a decrease of accuracy of 0.16° between calibration and later testing. This slow degradation of gaze tracking accuracy could impact the data quality in long-term studies. Therefore, the results we present here can help in planning such long-term gaze tracking studies.

Predicting Human Saccadic Scanpaths Based on Iterative Representation Learning

Visual attention is a dynamic process of scene exploration and information acquisition. However, existing research on attention modeling has concentrated on estimating static salient locations. In contrast, dynamic attributes presented by saccade have not been well explored in previous attention models. In this paper, we address the problem of saccadic scanpath prediction by introducing an iterative representation learning framework. Within the framework, saccade can be interpreted as an iterative process of predicting one fixation according to the current representation and updating the representation based on the gaze shift. In the predicting phase, we propose a Bayesian definition of saccade to combine the influence of perceptual residual and spatial location on the selection of fixations. In implementation, we compute the representation error of an autoencoder-based network to measure perceptual residuals of each area. Simultaneously, we integrate saccade amplitude and center-weighted mechanism to model the influence of spatial location. Based on estimating the influence of two parts, the final fixation is defined as the point with the largest posterior probability of gaze shift. In the updating phase, we update the representation pattern for the subsequent calculation by retraining the network with samples extracted around the current fixation. In the experiments, the proposed model can replicate the fundamental properties of psychophysics in visual search. In addition, it can achieve superior performance on several benchmark eye-tracking data sets.

A proto-object based saliency model in three-dimensional space

Most models of visual saliency operate on two-dimensional images, using elementary image features such as intensity, color, or orientation. The human visual system, however, needs to function in complex three-dimensional environments, where depth information is often available and may be used to guide the bottom-up attentional selection process. In this report we extend a model of proto-object based saliency to include depth information and evaluate its performance on three separate three-dimensional eye tracking datasets. Our results show that the additional depth information provides a small, but statistically significant, improvement in the model's ability to predict perceptual saliency (eye fixations) in natural scenes. The computational mechanisms of our model have direct neural correlates, and our results provide further evidence that proto-objects help to establish perceptual organization of the scene.