Locatizing Barcode using Scilab with [SVIP toolbox & IPD toolbox loaded]?

Divide image into NxM sub-images. Then folow either.

Divide image into NxM sub-images. Then folow either: a) Make FFT on each sub-image and compare result with pre-computed 2D FFT of standard barcode image. (Because barcodes are pretty monotone and highly repetitive images they should generate similar FFT patterns) b) Execute some thining (skeletonization) algorithm to leave only skeletons in images.

Then do Radon or Hough transform on each skeleton sub-image and look for bunch of lines which has same deflection angle withing error bounds. I personally would choose second way with pre-skeletonizing step because Radon/Hough transforms are especially designed for searching lines in images.

Sorry, I new here! So I don't really understand the terms! Anyway I will try google it and try!

Thanks! :) – Kim 4 hours ago.

If the barcode is the only significant thing in the image, you could do a Radon transform and then calculate the RMS of each rotation, and keep the row with the highest value.

Sorry, I new here! So I don't really understand the terms! Anyway I will try google it and try!

Thanks! :) – Kim 3 hours ago.

I cant really gove you an answer,but what I can give you is a way to a solution, that is you have to find the anglde that you relate to or peaks your interest. A good paper is one that people get drawn into because it reaches them ln some way.As for me WW11 to me, I think of the holocaust and the effect it had on the survivors, their families and those who stood by and did nothing until it was too late.