wiki:GSoC/2016/Additional_segmentation_algorithms/weekly_report

Version 35 (modified by hao2309, 8 years ago) ( diff )

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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 too get more familiar with basic GRASS codes

Currently i.segment only provides region-growth algorithm. By modifying the codes in parse_args.c I added three inputs each for the additional two algorithms-- mean-shift and watershed.

  • 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 were made 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. but with the help of community, the problem was 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
  1. 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, region-based mode merging again to refine the result.

  1. 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
  • 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.
  1. Codes will be implemented based on the essential functions of mean-shift algorithm.
  2. The fixed bandwidth and range width will be used.
  3. Codes need to implement the function which is able to separate objects(super-pixel).

Are you blocked on anything?

  • Not for now
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