Some exercise for an exam. (Trying to not use openCV implementation)

Use script.sh to build cpp files

Canny algorithm for edge detection. Pass Gaussian blur kernel, sigma, lower threshold, higher threshold.

Used for distance mapping.

Equalize an image histogram.

Algorithm for Corner detection in an image.

Detection circles in images: minimum radius, max radius and threshold for accumulator.

Detection lines in images: input is the threshold for accumulator.

K-means algorithm find correct cluster starting from K centroids: applied to images find correct cluster for a pixel based on RGB distance (euclidian). Auto version stops if there isn't a variation under a certain threshold. Interactive can select with mouse centroids rather then random. Iterative use N iteration before stop.

Apply a median blur for salt pepper noise images or mean blur for noisy images.

Region growing using RGB distance and HSV tint for just a color expansion. Interactive must click on source image to expand. RGB and Greyscale version tries to expand automatically.

Recursively split images if RGB dist is less then threshold. Try to recolor blocks up to max block size.