Figure 12
From: A neural computational model for bottom-up attention with invariant and overcomplete representation
Invariance to orientation. (a) an example of images where orientation noises are in [−20°, 20°] with uniform probability; (b) saliency map of our model for (a); (c) saliency map of Saliency Tool for (a); (d) an example of images where orientation noises are in [−30°, 30°] with uniform probability; (e) saliency map of our model for (d); (f) saliency map of Saliency Tool for (d); (g) statistics of correct detection rate vs. range of orientation noises |Δθ| in distractors (that is, when the orientation of the target is θ t , the orientations of distractors vary in [θ t + pi/2−Δθ,θ t + pi/2 + Δθ]). For each |Δθ|, we test 20 randomly generated images. The target is marked by a red circle. The results of Saliency Tool are got by only using orientation information. Note that since orientation difference between the target and distractors is pi/2, |Δθ| should not exceed 45°.