MCS Project - Enhanced Watershed
- 1. Enhanced Watershed Image Processing Segmentation Aamir Shahzad CIIT/SP05-MCS-002/WAH
- 2. Abstract Watershed enhancement can be done in three different ways. One is to perform preprocessing. Second is to improve the watershed algorithm. And the third one is to perform post processing. I choose the last option for enhancement because this option has more opportunities for enhancement. In this option, I use the technique “Fusion of edge based enhanced watershed segmentation” or “edge based enhanced watershed segmentation”.
- 3. What is Watershed Segmentation? What is segmentation? Understanding the watershed transform requires that you think of an image as a surface. The key behind watershed is to change your image into another image whose catchment basins are the objects you want to identify.
- 4. Marker-Controlled Watershed In simple watershed, we face the problem of over segmentation. Marker control is a improved form of watershed. Here we use internal and external markers define by automatically or from user
- 5. Proposed system – enhanced watershed segmentation Following are the high level main steps Perform watershed segmentation on main image Get edge image from main image Enhance edge image results Merge the results based on the best results
- 6. Algorithms used in main algorithm Connect edge with border Fill one missing pixel Get big object number Connect object with border Get minimum object size Get object start pixel Get object number Get object size Get select object
- 7. Proposed algorithm Read image Convert image to gray scale, if required Perform canny edge detection and get edges connect edges with border Fill missing pixel in edges make edges logical (i.e. 0/1) Complement the image Perform labeling function on edges and get label 1 and total objects in label 1 Get biggest object number in the label 1 Connect objects with border Perform labeling function again and get label 1 and total objects in label 1 Get biggest object number again in the label 1 Perform existing watershed method and get the label 2 Perform labeling function on label 2 and get total objects in label 2 Get biggest object number in the label 2 Get the size of minimum object in label 2 loop through 1st object to total object in label 1 if current object number is equal to biggest number in label 1 then continue Get the current object’s start pixel in x and y variable from label 1 Get the object number at x and y position in label 2 if object number is equal to the biggest object number of label 2 then Increase the value of total objects in label 2 by one Find the rows and columns pixels of current object in label 1 Find the total pixels (i.e. total rows or columns) in above find rows and columns
- 8. Loop through 1st pixel to the last pixel of current object in label 1 Change the current pixel value at label 2 to total objects value in label 2 if there is other objects pixel in between rows then change the pixel to total objects value continue the loop at 17 Get the current object size from label 1 Get the size of object number (see 17.c) If current object’s size is greater than object number’s size then Find the rows and columns pixels of current object in label 1 Find the total pixels (i.e. total rows or columns) in above find rows and columns Loop through 1st pixel to the last pixel of current object in label 1 Change the current pixel value at label 2 to object number value (see 17.c) continue the loop at 17 If current object’s size is greater than double size of minimum object in label 2 Find the rows and columns pixels of current object in label 1 Find the total pixels (i.e. total rows or columns) in above find rows and columns Increase the value of total objects in label 2 by one Loop through 1st pixel to the last pixel of current object in label 1 Change the current pixel value at label 2 to total object value if there is other objects pixel in between rows then change the pixel to total objects value continue the loop at 17 Convert the label2 to RGB and display the final enhanced watershed result
- 9. Example 1 Original Image Simple Watershed Result Marker-Controlled Watershed Result My Watershed Result
- 10. Example 2 Original Image Marker-Controlled Watershed Result My Watershed Result
- 11. Example 3 Original Image Marker-Controlled Watershed Result My Watershed Result
- 12. Example 4 Original Image Marker-Controlled Watershed Result My Watershed Result
- 13. Note: Results approximate 1% 20% 20% 0% 6 1% 30% 30% 0% 5 1% 70% 70% 0% 4 1% 10% 50% 40% 3 3% 20% 70% 50% 2 My watershed Watershed 1% 10% 80% 70% 1 Fault Enhancement Image No
- 14. 2% 30% 30% 0% 12 1% 705 70% 0% 11 1% 90% 90% 0% 10 2% 25% 30% 5% 9 1% 3% 53% 50% 8 My watershed Watershed 1% 70% 70% 0% 7 Fault Enhancement Image No
- 15. << The End >> 2% 36% 54% 18% Average 10% 0% 25% 30% 15 1% 90% 90% 0 % 14 My watershed Watershed 1% 5% 35% 30% 13 Fault Enhancement Image No