Figure 8
From: A neural computational model for bottom-up attention with invariant and overcomplete representation
Saliency detection performances on real images under different SNRs (a) 3 examples of testing images. Up: the banana in terrain background at the SNR 1:3, mid: the pear in brick background at the SNR 1:2.5, down:the red onion in water background at SNR of 1:3.5; (b) saliency maps of the attention model with invariant coding; (c) without invariant coding; (d) saliency maps of the Saliency Tool. In a saliency map, the more salient part is indicated by the brighter and whiter region; (e) plot of the correct detection rates versus SNRs. Larger RF size improves the performance of the model with invariant coding, which coincides with findings in neuroscience.