median_fg: float
d_fg: Image<Gray, float32> } // Euclidean distances of the foreground to median_fg.
-let kmedians (img: Image<Gray, float32>) (fgFactor: float) : Result =
- let nbIteration = 3
+let kmedians (img: Image<Gray, float32>) : Result =
+ let nbIteration = 4
let w = img.Width
let h = img.Height
let mutable fg = new Image<Gray, byte>(img.Size)
for i in 1 .. nbIteration do
- CvInvoke.Pow(img - median_bg, 2.0, d_bg)
- CvInvoke.Pow(img - median_fg, 2.0, d_fg)
+ d_bg <- img.AbsDiff(Gray(median_bg))
+ d_fg <- img.AbsDiff(Gray(median_fg))
+
CvInvoke.Compare(d_fg, d_bg, fg, CvEnum.CmpType.LessThan)
let bg_values = List<float>()
median_bg <- MathNet.Numerics.Statistics.Statistics.Median(bg_values)
median_fg <- MathNet.Numerics.Statistics.Statistics.Median(fg_values)
- CvInvoke.Sqrt(d_fg, d_fg)
-
{ fg = fg; median_bg = median_bg; median_fg = median_fg; d_fg = d_fg }