Fundamental Limits of Bayesian Inference: Order Parameters and Phase Transitions for Road Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying fixations and saccades in eye-tracking protocols
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Information Theory in Computer Vision and Pattern Recognition
Information Theory in Computer Vision and Pattern Recognition
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In this paper we propose an information-theoretic approach to understand eye-movement patterns, in relation to the task performed and image complexity. We commence with the analysis of the distributions and amplitudes of eye-movement saccades, performed across two different image-viewing tasks: free viewing and visual search. Our working hypothesis is that the complexity of image information and task demands should interact. This should be reflected in the Markovian pattern of short and long saccades. We compute high-order Markovian models of performing a large saccade after many short ones and also propose a novel method for quantifying image complexity. The analysis of the interaction between high-order Markovianity, task and image complexity supports our hypothesis.