Otsu Clustering #27
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Otsu Clustering #27
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We first extract some information from the image - width, height etc., convert to luma and compute gradients. Then, for each pixel (x,y), we compute rho = xcos(theta) + ysin(theta). After a few more transformations and finding the bins with highest values, we generate the resulting image.
Preliminary Hough transform implementation
It is used to improve contrast in images and works by performing enhancement on small 'contextual' regions or neighborhood of each pixel in the given image.
Adaptive Histogram Equalization
Laplacian filter
Fix the horizontal kernel of Sobel filter.
* Mean filter version 1
lehins
reviewed
Aug 19, 2018
| @@ -46,6 +46,8 @@ Library | |||
| , vector >= 0.10 | |||
| , array | |||
| , random | |||
| , random-fu | |||
| , normaldistribution | |||
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Do we really need to use 3 different packages that do random numbers?
lehins
reviewed
Aug 19, 2018
| import Data.Vector.Unboxed as V | ||
| import Data.List as L | ||
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| -- the tail is safe because scanl alawys returns a nonempty list |
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That statement is incorrect. dropWhile could make that list empty, so it is important to know that there is at least one element in the list with wF == 0. For example, this function will produce an error if hist is empty.
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An initial version of Otsu Clustering algorithm. A few changes yet to be made before producing sample outputs.