Title :
Enhanced image saliency model based on blur identification
Author :
Khan, R.A. ; Konik, H. ; Dinet, É
Author_Institution :
Lab. Hubert Curien, Univ. Jean Monnet, St. Étienne, France
Abstract :
Detection of visual saliency is of great interest for a lot of computer vision applications in particular for content-based image retrieval. The work presented in this paper is devoted to develop an algorithm of saliency detection that performs adequately in predicting human fixations for stimuli containing blur and sharp regions. This work is based on an experimental study on the effect of blurriness on visual attention when observers see images with no prior knowledge in free viewing conditions. A ground-truth has been derived from this experimental study to test the saliency model we developed.
Keywords :
computer vision; content-based retrieval; image enhancement; image restoration; image retrieval; blur identification; computer vision; content-based image retrieval; enhanced image saliency model; human fixations; visual saliency; Computers; Context; Image edge detection; Laplace equations; Strontium; Tracking; Visualization; blur detection; colour image processing; visual saliency;
Conference_Titel :
Image and Vision Computing New Zealand (IVCNZ), 2010 25th International Conference of
Conference_Location :
Queenstown
Print_ISBN :
978-1-4244-9629-7
DOI :
10.1109/IVCNZ.2010.6148833
