I have been searching for some good papers which I could implement to improve my current implementation. After lots of googling and reading few papers, my eyes were half red. It was around 3 a.m. when I came across Ioannis Katramados and Toby Breckon’s paper on “REAL-TIME VISUAL SALIENCY BY DIVISION OF GAUSSIANS”. This was not exactly what I was looking for but it somehow seems to satisfy my need. So I thought to implement the paper. Since then I have been discussing various things with both I. Katramados and T. Breckon. They both are really helpful and so is their paper.

Coming back to implementation, the paper has been written perfectly and it seems easy to implement the paper using OpenCV (SEEMS!).

1) I converted image to GrayScale (32F)2) according to step one in paper: “The Gaussian pyramid U comprises of n levels,starting with an image U1 as the base with resolution w × h. Higher pyramid levels are derived via downsampling using a 5 × 5 Gaussian filter. The top pyramid level has a resolution of (w/2n−1 ) × (h/2n−1 ). Let us call this image Un .“which means I simply have to perform pyrDown() operation in opencv. I did it 8 times.3) according to step two paper: “U n is used as the top level Dn of a second Gaus-sian pyramid D in order to derive its base D1 . In this case, lower pyramid levels are derived via upsampling using a 5×5 Gaussian filter.”I simply performed 8 times pyrUp image.4) And then goes pixel by pixel division of values as according to paper.5) I normalized the result matrix to 0-255