Eye movement is the simplest and repetitive movement that enables humans to interact with the environment. The common daily activities, such as reading a book or watching television, involve this natural activity, which consists of rapidly shifting our gaze from one region to another. In clinical application, the identification of the main components of eye movement during visual exploration, such as fixations and saccades, is the objective of the analysis of eye movements: however, in patients affected by motor control disorder the identification of fixation is not banal. This work presents a new fixation identification algorithm based on the analysis of variance and covariance: the main idea was to use bivariate statistical analysis to compare variance over x and y to identify fixation. We describe the new algorithm, and we compare it with the common fixations algorithm based on dispersion. To demonstrate the performance of our approach, we tested the algorithm in a group of healthy subjects and patients affected by motor control disorder.

Veneri, G., Piu, P., Rosini, F., Federighi, P., Federico, A., & Rufa, A. (2011). Automatic eye fixations identification based on analysis of variance and covariance,. PATTERN RECOGNITION LETTERS, 32(2011), 1588-1593 [10.1016/j.patrec.2011.06.012].

Automatic eye fixations identification based on analysis of variance and covariance,

ROSINI, FRANCESCA;FEDERIGHI, PAMELA;FEDERICO, ANTONIO;RUFA, ALESSANDRA
2011

Abstract

Eye movement is the simplest and repetitive movement that enables humans to interact with the environment. The common daily activities, such as reading a book or watching television, involve this natural activity, which consists of rapidly shifting our gaze from one region to another. In clinical application, the identification of the main components of eye movement during visual exploration, such as fixations and saccades, is the objective of the analysis of eye movements: however, in patients affected by motor control disorder the identification of fixation is not banal. This work presents a new fixation identification algorithm based on the analysis of variance and covariance: the main idea was to use bivariate statistical analysis to compare variance over x and y to identify fixation. We describe the new algorithm, and we compare it with the common fixations algorithm based on dispersion. To demonstrate the performance of our approach, we tested the algorithm in a group of healthy subjects and patients affected by motor control disorder.
Veneri, G., Piu, P., Rosini, F., Federighi, P., Federico, A., & Rufa, A. (2011). Automatic eye fixations identification based on analysis of variance and covariance,. PATTERN RECOGNITION LETTERS, 32(2011), 1588-1593 [10.1016/j.patrec.2011.06.012].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/11819
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