Issue No. 11 - Nov. (2013 vol. 19)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2013.99
Li-Qian Ma , Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Kun Xu , Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Tien-Tsin Wong , Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, China
Bi-Ye Jiang , Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Shi-Min Hu , Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Change blindness refers to human inability to recognize large visual changes between images. In this paper, we present the first computational model of change blindness to quantify the degree of blindness between an image pair. It comprises a novel context-dependent saliency model and a measure of change, the former dependent on the site of the change, and the latter describing the amount of change. This saliency model in particular addresses the influence of background complexity, which plays an important role in the phenomenon of change blindness. Using the proposed computational model, we are able to synthesize changed images with desired degrees of blindness. User studies and comparisons to state-of-the-art saliency models demonstrate the effectiveness of our model.
Blindness, Visualization, Computational modeling, Complexity theory, Image color analysis, Psychology, Context modeling
Li-Qian Ma, Kun Xu, Tien-Tsin Wong, Bi-Ye Jiang and Shi-Min Hu, "Change Blindness Images," in IEEE Transactions on Visualization & Computer Graphics, vol. 19, no. 11, pp. 1808-1819, 2013.