Thresholding
{\label{eq.1}} F=\alpha A+\beta W=\beta(\frac{\alpha}{\beta}A+W)
\begin{equation}{\label{eq.1}} F=\alpha A+\beta W=\beta(\frac{\alpha}{\beta}A+W) \end{equation},
A Short Summary of Thresholding Algorithm
The basic concept of thresholding: to segment objects from the background based on intensity values.
A simple method is making the output result as a binary image as
Multiple thresholding
OTSU’s method.
The aim is to maximize the between-class variance based on the histogram of an image
Let us define the mean intensity of the entire image as
which is equivalent to
Note: Bayes formula
*
Thus, we can express the total mean intensity as
larger value of η.
To make the calculation simpler, we transform the formula as
The Procedure of Otsu Method
Apply an image filter prior to thresholding.
Local thresholding
Method 1. Image partitioning
Subdivide an image into non overlapping rectangles. Apply otsu threshold in each sub division.
Works well when the objects and background occupy reasonably comparable size.
But fails if either object or background is small.
Method 2. Based on local image property
preferable if background is nearly uniform.
Method 3. Moving average.
Scan line by line in zigzag to reduce illumination effect
See example with text image corrupted by spot shading
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