Improved saliency toolbox/Itti model for region of interest extraction

Dong-Jian He; Shang-Wang Liu; Xin-Hong Liang; Cheng Cai

doi:10.1117/1.3625422

1 September 2011 Improved saliency toolbox/Itti model for region of interest extraction

Dong-Jian He, Shang-Wang Liu, Xin-Hong Liang, Cheng Cai

Author Affiliations +

Funded by: National Natural Science Foundation of China, National Science Foundation of China (NSFC)

Optical Engineering, Vol. 50, Issue 9, 097202 (September 2011). https://doi.org/10.1117/1.3625422

Abstract

The saliency toolbox (STB)/Itti model is an outstanding computational selective visual attention model. In this paper, we propose an improved STB/Itti model to overcome the drawback of STB/Itti-its output "saliency map" is not large enough for region of interest (ROI) extraction. First, we employ a simplified pulse coupled neural network (PCNN) with a special input image, and more importantly, the PCNN does not require iterations. Subsequently, the PCNN takes the place of the winner-take-all network in STB/Itti. Experimental results show that the improved STB/Itti model works well for ROI extraction, with the mean area under the curve value of 0.8306 and robustness against noise and geometric attacks. The proposed model can greatly enhance the performances of both STB/Itti and PCNN model in image processing.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Dong-Jian He, Shang-Wang Liu, Xin-Hong Liang, and Cheng Cai "Improved saliency toolbox/Itti model for region of interest extraction," Optical Engineering 50(9), 097202 (1 September 2011). https://doi.org/10.1117/1.3625422

Published: 1 September 2011

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