GRASS GSoC 2016 Additional Image Segmentation Algorithms for i.segment

Student Name:

Bo Yang

Organization:

​OSGeo - Open Source Geospatial Foundation

Mentors:

Moritz Lennert, Markus Metz

Title:

​Additional segmentation algorithms for i.segment

Repository:

GRASS 7, browse at: ​i.segment sandbox

16 – 21 May week 0: Setup coding environmental, get familiar with programming manual

What did you get done this week?

• Finished a small exercise to get more familiar with basic GRASS codes

Currently i.segment only provides region-growth algorithm. By modifying ​parse_args.c I added three inputs for additional two algorithms, mean-shift and watershed, respectively.

• Reviewed some literature for mean-shift algorithm

1. Deng, C., Li, S., Bian, F., & Yang, Y. (2015). Remote Sensing Image Segmentation Based on Mean, (1999), 179–185.
2. Michel, J., Youssefi, D., & Grizonnet, M. (2015). Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 53(2), 952–964. ​http://doi.org/10.1109/TGRS.2014.2330857
3. Zhang, Q., Liu, C., Zhang, G., & Zhou, A. (2014). Adaptive image segmentation by using mean-shift and evolutionary optimisation. IET Image Processing, 8(6), 327–333. ​http://doi.org/10.1049/iet-ipr.2013.0195
4. Zhou, J.-X., Li, Z.-W., & Fan, C. (2015). Improved fast mean shift algorithm for remote sensing image segmentation. IET Image Processing, 9(5), 389–394. ​http://doi.org/10.1049/iet-ipr.2014.0393

• Some discussions with mentors about the algorithm and literature

What do you plan on doing next week?

• Make clear understanding about the algorithm mechanism and write the pseudo codes for prototyping.

Are you blocked on anything?

• Some issues happened during the compiling of the GRASS in Windows environmental. With the help fromcommunity, ​the problem has been later solved.

23 - 28 May week 1: Start coding, develop pseudo-code to outline the work

What did you get done this week?

• Further discussion about the algorithm mechanism

1. Edge effect:

Moving window have to be re-sized when it near the edge or corner of the full image. Mentor has given the solution in pseudo-code:

      # figure out moving window, clip to region if necessary
      mwrow1 = row - (int)radius
      mwrow2 = mwrow1 + window_size
      if (mwrow1 < 0)
        mwrow1 = 0
      if (mwrow2 > nrows)
        mwrow2 = nrows
      
      mwcol1 = col - (int)radius
      mwcol2 = mwcol1 + window_size
      if (mwcol1 < 0)
        mwcol1 = 0
      if (mwcol2 > ncols)
        mwcol2 = ncols

2. Adaptive bandwidth:

Earlier mean-shift algorithm uses fixed bandwidth. Fixed bandwidth could result in either over-segment or under-segment. In the literature of Deng et al., 2015 and ​Zhang et al., 2014, each paper proposed an adaptive bandwidth method. ​Zhou et al., 2015 proposed another method, which use smaller bandwidth at first to over-segment the image, then use image clustering and region-based mode merging again to refine the result.

3. The convergence condition:

Set an epsilon value, e.g. 0.001. If the pixel value difference is less than epsilon between iterations, the pixel is considered as convergent.

• Got the access for GRASS-addons-svn and ​sandbox

• Wrote the ​pseudo-code for the mean-shift algorithm

• Mentors reviewed pseudo-code and send the improved ​version

What do you plan on doing next week?

• Write the meanshift.c module to implement the mean-shift algorithm based on pseudo-code. Codes will be implemented based on the essential functions of mean-shift algorithm. The fixed bandwidth and rangewidth will be used and codes need to be implemented to separate objects (super-pixel).

Are you blocked on anything?

• Not for now

30 May -- 4 June week 2: implement mean-shift image segmentation algorithm

What did you get done this week?

• Implemented the basic mean-shift.c algorithm in ​sandbox according to the pseudo-code. A 8x8 ascii raster from stackoverflow have been used for testing the mean-shift.c basic algorithm, the original image are:

  103 103 103 103 103 103 106 104   
  103 147 147 153 147 156 153 104   
  107 153 153 153 153 153 153 107   
  103 153 147  96  98 153 153 104   
  107 156 153  97  96 147 153 107   
  103 153 153 147 156 153 153 101   
  103 156 153 147 147 153 153 104   
  103 103 107 104 103 106 103 107
  

after the mean-shift iteration:

103.99  104.00  104.01  104.04  104.07  104.11  104.15  104.20
104.00  151.86  151.86  151.87  151.88  151.89  151.89  104.24
104.01  151.86  151.86  151.87  151.88  151.89  151.90  104.28
104.03  151.85  151.86   96.75   96.75  151.90  151.90  104.31
104.06  151.85  151.86   96.75   96.75  151.89  151.90  104.34
104.09  151.85  151.86  151.87  151.88  151.89  151.89  104.36
104.13  151.86  151.86  151.87  151.88  151.89  151.89  104.36
104.17  104.20  104.24  104.28  104.31  104.33  104.35  104.36 

Then the image has been converted to segmentation image with object ID:

1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00
1.00    2.00    2.00    2.00    2.00    2.00    2.00    1.00
1.00    2.00    2.00    2.00    2.00    2.00    2.00    1.00
1.00    2.00    2.00    3.00    3.00    2.00    2.00    1.00
1.00    2.00    2.00    3.00    3.00    2.00    2.00    1.00
1.00    2.00    2.00    2.00    2.00    2.00    2.00    1.00
1.00    2.00    2.00    2.00    2.00    2.00    2.00    1.00
1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00

What do you plan on doing next week?

• integrate the current basic mean-shift C codes to i.segment, make the mean-shift algorithm run using the GUI and command line, rather than stand-alone program.
  1. Add the parameters to both parse_args.c and iseg.h.
  2. Based on mentors template ​mean_shift.c fill in the codes to implement the algorithm.
  3. Refer to the r.clump/clump.c to implement the segment object function.
  4. Merger the smaller super-pixels to adjacent super-pixel which under the threshold and write the output band

Are you blocked on anything?

• Not for now