Motion integration in visual attention models for predicting simple dynamic scenes

A. Bur; P. Wurtz; R. M. Müri; H. Hügli

doi:10.1117/12.704185

16 February 2007 Motion integration in visual attention models for predicting simple dynamic scenes

A. Bur, P. Wurtz, R. M. Müri, H. Hügli

Proceedings Volume 6492, Human Vision and Electronic Imaging XII; 649219 (2007) https://doi.org/10.1117/12.704185
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

Visual attention models mimic the ability of a visual system, to detect potentially relevant parts of a scene. This process of attentional selection is a prerequisite for higher level tasks such as object recognition. Given the high relevance of temporal aspects in human visual attention, dynamic information as well as static information must be considered in computer models of visual attention. While some models have been proposed for extending to motion the classical static model, a comparison of the performances of models integrating motion in different manners is still not available. In this article, we present a comparative study of various visual attention models combining both static and dynamic features. The considered models are compared by measuring their respective performance with respect to the eye movement patterns of human subjects. Simple synthetic video sequences, containing static and moving objects, are used to assess the model suitability. Qualitative and quantitative results provide a ranking of the different models.

Citation Download Citation

A. Bur, P. Wurtz, R. M. Müri, and H. Hügli "Motion integration in visual attention models for predicting simple dynamic scenes", Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 649219 (16 February 2007); https://doi.org/10.1117/12.704185

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available