30-07-2012, 04:33 PM
Digital Image Processing
[attachment=29585]
Thresholding
Thresholding is usually the first step in any segmentation approach
We have talked about simple single value thresholding already
Single value thresholding can be given mathematically as follows.
Basic Global Thresholding
Based on the histogram of an image
Partition the image histogram using a single global threshold
The success of this technique very strongly depends on how well the histogram can be partitioned.
Basic Global Thresholding Algorithm
The basic global threshold, T, is calculated
as follows:
Select an initial estimate for T (typically the average grey level in the image)
Segment the image using T to produce two groups of pixels: G1 consisting of pixels with grey levels >T and G2 consisting pixels with grey levels ≤ T
Compute the average grey levels of pixels in G1 to give μ1 and G2 to give μ2
Basic Adaptive Thresholding
An approach to handling situations in which single value thresholding will not work is to divide an image into sub images and threshold these individually
Since the threshold for each pixel depends on its location within an image this technique is said to adaptive
Summary
In this lecture we have begun looking at segmentation, and in particular thresholding
We saw the basic global thresholding algorithm and its shortcomings
We also saw a simple way to overcome some of these limitations using adaptive thresholding
[attachment=29585]
Thresholding
Thresholding is usually the first step in any segmentation approach
We have talked about simple single value thresholding already
Single value thresholding can be given mathematically as follows.
Basic Global Thresholding
Based on the histogram of an image
Partition the image histogram using a single global threshold
The success of this technique very strongly depends on how well the histogram can be partitioned.
Basic Global Thresholding Algorithm
The basic global threshold, T, is calculated
as follows:
Select an initial estimate for T (typically the average grey level in the image)
Segment the image using T to produce two groups of pixels: G1 consisting of pixels with grey levels >T and G2 consisting pixels with grey levels ≤ T
Compute the average grey levels of pixels in G1 to give μ1 and G2 to give μ2
Basic Adaptive Thresholding
An approach to handling situations in which single value thresholding will not work is to divide an image into sub images and threshold these individually
Since the threshold for each pixel depends on its location within an image this technique is said to adaptive
Summary
In this lecture we have begun looking at segmentation, and in particular thresholding
We saw the basic global thresholding algorithm and its shortcomings
We also saw a simple way to overcome some of these limitations using adaptive thresholding