Scilab implementation for Canny's algorithm.
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README.md

Canny's algorithm: detect edges on a picture

Scilab implementation of the famous Canny's algorithm.

The purpose of this project is to treat a given image to find its edges, and to create a new image to display them.

Steps

  1. Normalize given image: application of a Gaussian filter.
  2. Compute gradient and gradient norm (E_s_,E_o_).
  3. Delete non-maximum values from the image.
  4. Hysteresis thresholding.
  5. Display result (concat given image and result image).

Note that the hysteresis threshold can be changed: for a good value, pick something between 70% and 85%.

Main functions

  1. applyMask(imgMatrix,mask) : apply a given mask to a given image. Mask can look like [1,2,1;2,4,2;1,2,1].
  2. hysteresisThreshold(image,Eo,Es,95); : compute hysteresis threshold for the given image, last parameter is the threshold you want to pick.

Warning

  • Possible issues if you use Scilab x64 (Scilab x32 preferred).
  • Don't forget to increase the stack size! stack size('max');

How to use this code?

  • Clone project.
  • Run script.
    • img = loadImage('./img3.jpg',1); l.372: Choose image to load.
    • mask = [2,4,5,4,2;4,9,12,9,4;5,12,15,12,5;4,9,12,9,4;2,4,5,4,2]; l.375: init your mask.
    • writeImage(uint8(resultImage),'./tests/img3_res_95.jpg'); l.395: write result of execution on disk.
  • Done!
  • Tests & results of previous executions provided in "./tests" folder.

Results & samples

Results of several execution can be found in "./tests" folder.

Result for image1, thresold 86%