![ Selection of proper threshold is essential for every threshold based
segmentation technique. This threshold value of the thresholding
operation can be considered as an operation that invokes testing
against a function T where this function T is of the form
T = T[(x, y), p(x, y), f(x, y)]
where, (x, y) ⇒Pixel Location p(x, y) ⇒Local property in a
neighbourhood cantered at ( x , y ). f(x, y) ⇒ Pixel intensity at ( x , y
).
9](https://image.slidesharecdn.com/imagesegusingthresholding-181213074523/85/Image-seg-using_thresholding-9-320.jpg)
![ If the threshold T is only a function of pixel intensity value f ( x ,y ).
Then T is termed as global threshold.
T [f(x,y)] ⇒ Global Threshold
Threshold T is termed as local threshold if T is a function of pixel
intensity value and local property.
T[f(x,y),p(x,y)] ⇒ Local Threshold
If the threshold is a function of all the three properties then T is
termed as adaptive threshold.
T[(x,y),f(x,y),p(x,y)] ⇒ Adaptive Threshold
11](https://image.slidesharecdn.com/imagesegusingthresholding-181213074523/85/Image-seg-using_thresholding-11-320.jpg)
Image seg using_thresholding
- 1. Under the guidance of: Dr. Subrajeet. Mohapatra Presented by Vani A. Hiremani PhD|CSE|10003|2018 1
- 2. Image Segmentation Image Segmentation classification Thresholding Automatic thresholding algorithm Example of Thresholding Conclusion 2
- 3. Image Segmentation is a procedure that describes the process of dividing an image into non overlapping, connected image areas, called regions, on the basis of criteria governing similarity and homogeneity. 3 Segmented
- 4. Discontinuity based o Detection of Isolated Points o Detection of Lines o Edge Detection Similarity based o Thresholding o Region growing o Region Splitting and Merging o Clustering 4
- 5. Thresholding is a technique of segmenting the a binary image based upon a threshold value. Image thresholding is very useful for object extraction and background rejection. Belongingness of each pixel to object or background is decided on the basis of a particular threshold. 5
- 6. Image histogram describes the frequency of the intensity values that occur in an image. Histogram can be very efficiently used for determining the threshold for image segmentation. 6
- 7. Ideal bimodal histogram consists of peaks corresponding to the object and background regions and a valley in between. The object and background of images with bimodal histogram form two different groups with distinct gray levels. Bi–level thresholding is employed for such images. So a threshold T has to be selected from the valley region for segmenting the image. 7 Peak 1 background Peak 2 object <T<
- 8. A single threshold is enough for segmenting an image with bimodal histogram and is called bi–level thresholding. For an image f ( x , y ) with an bright object and dark background, the binary segmented image can be mathematically represented as g ( x ,y )= 1 if f ( x ,y ) ≥ T ⇒Object 0 if f ( x ,y ) < T ⇒ Background Every pixel intensity value has to be compared with the threshold T to classify each pixel as a background or an object pixel. 8
- 9. Selection of proper threshold is essential for every threshold based segmentation technique. This threshold value of the thresholding operation can be considered as an operation that invokes testing against a function T where this function T is of the form T = T[(x, y), p(x, y), f(x, y)] where, (x, y) ⇒Pixel Location p(x, y) ⇒Local property in a neighbourhood cantered at ( x , y ). f(x, y) ⇒ Pixel intensity at ( x , y ). 9
- 10. So in general this threshold T can be a function of pixel Location, local property within the neighbourhood and pixel intensity value. Threshold T can be a function of any combination of the above three terms. Depending on this combination the threshold T can be classified as ◦ Global Threshold ◦ Local Threshold ◦ Adaptive Threshold 10
- 11. If the threshold T is only a function of pixel intensity value f ( x ,y ). Then T is termed as global threshold. T [f(x,y)] ⇒ Global Threshold Threshold T is termed as local threshold if T is a function of pixel intensity value and local property. T[f(x,y),p(x,y)] ⇒ Local Threshold If the threshold is a function of all the three properties then T is termed as adaptive threshold. T[(x,y),f(x,y),p(x,y)] ⇒ Adaptive Threshold 11
- 12. Using this threshold T we want to get a Thresholded binary image g ( x , y ) defined as g ( x ,y )= 1 if f ( x ,y ) ≥ T ⇒ Object 0 if f ( x ,y ) < T ⇒ Background This threshold T can be global, local or adaptive. 12
- 13. Step 1: Select an initial value of threshold T . Step 2: Use T to segment the image into two groups G 1& G 2 Step 3: Compute the mean µ1 and µ2 for each group of pixels. Step 4: Compute the new updated threshold T using the relation T = µ1 + µ2 Step 5: Repeat step 2-4 until the mean values µ1 and µ2 in successive iterations do not change. 13
- 14. Image segmentation is an essential preliminary step in image analysis and interpretation. There is no universal algorithm or segmentation technique for all kind of images. Specific methods have to be developed for segmenting particular kind of images. None of the segmentation evaluation measure are perfect. 14