How people look at visual information reveals fundamental information about themselves,
their interests and their state of mind. While previous visual attention models output static 2D …

Knowledge of the human visual system helps to develop better computational models of
visual attention. State-of-the-art models have been developed to mimic the visual attention …

Due to the limited capability for information processing, humans only choose a small amount
of input data received from visual field to better understand their environment. The selection …

Previous research showed the existence of systematic tendencies in viewing behavior
during scene exploration. For instance, saccades are known to follow a positively skewed …

Visual attention is a field with a considerable history, with eye movement control and
prediction forming an important subfield. Fixation modeling in the past decades has been …

Visual attention is a dynamic process of scene exploration and information acquisition.
However, existing research on attention modeling has concentrated on estimating static …

In this paper, we present a unified statistical framework for modeling both saccadic eye
movements and visual saliency. By analyzing the statistical properties of human eye …

We present a model for predicting eye movements of an observer viewing dynamic scenes.
Supervised-learning techniques are used to tune the model for a particular observer. The …

A large body of previous models to predict where people look in natural scenes focused on
pixel-level image attributes. To bridge the semantic gap between the predictive power of …

Gaze-contingent display paradigms play an important role in vision research. The time delay
due to data transmission from eye tracker to monitor may lead to a misalignment between …