![Optimum Global Thresholding Using Otsu’s
Method
• Otsu’s method (1979) maximizes between-class variance
• Based entirely on computations performed on histogram (1-D) of
image
• Normalized histogram: pi = ni MN, i = 0, . . . , L − 1, with L − 1 i=0 pi =
1, pi > 0
• Select threshold T ( k ) to segment image −→ Class C 1 (values [0, k])
Class C2 (values [k + 1, L − 1] )
⇒ Prob of pixel assigned to C 1 (ie of C 1 occuring):](https://image.slidesharecdn.com/dipppt-250209111413-af8bea2d/85/Digital-Image-Processing-Global-Thresholding-Using-Otsu-sMethod-3-320.jpg)
Digital Image Processing Global Thresholding Using Otsu’sMethod
- 3. Optimum Global Thresholding Using Otsu’s Method • Otsu’s method (1979) maximizes between-class variance • Based entirely on computations performed on histogram (1-D) of image • Normalized histogram: pi = ni MN, i = 0, . . . , L − 1, with L − 1 i=0 pi = 1, pi > 0 • Select threshold T ( k ) to segment image −→ Class C 1 (values [0, k]) Class C2 (values [k + 1, L − 1] ) ⇒ Prob of pixel assigned to C 1 (ie of C 1 occuring):
- 4. Optimum Global Thresholding Using Otsu’s Method
- 6. 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
- 7. Basic Global Thresholding Algorithm The basic global threshold, T, is calculated as follows: 1. Select an initial estimate for T (typically the average grey level in the image) 2. 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 3. Compute the average grey levels of pixels in G1 to give μ1 and G2 to give μ2
- 8. Basic Global Thresholding Algorithm 4. Compute a new threshold value: 5. Repeat steps 2 – 4 until the difference in T in successive iterations is less than a predefined limit T∞ This algorithm works very well for finding thresholds when the histogram is suitable 2 2 1 T