Improved saliency toolbox/Itti model for region of interest extraction
Dongjian He, Shang-Wang Liu, Xin-Hong Liang, Cheng Cai · Optical Engineering · 2011
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.