In this presentation, we present saccadic models which are an alternative way to predict where observers look at. Compared to saliency models, saccadic models generate plausible visual scanpaths from which saliency maps can be computed. In addition these models have the advantage of being adaptable to different viewing conditions, viewing tasks and types of visual scene. We demonstrate that saccadic models perform better than existing saliency models for predicting where an observer looks at in free-viewing condition.

演讲者简介：

Olivier Le Meur received the Ph.D. degree from University of Nantes, Nantes, France, in 2005.He was with the Media and Broadcasting Industry from 1999 to 2009. In 2003, he joined the Research Center of Thomson-Technicolor, Rennes, France, where he supervised a Research Project concerning the modeling of human visual attention. He has been an Associate Professor of image processing with the University of Rennes 1 since 2009. He currently works at the SIROCCO Team of IRISA/INRIA-Rennes, and his current research interests include human visual attention, computational modeling of visual attention, and saliency-based applications, such as video compression, objective assessment of video quality, and retargeting.

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