propagated segmentation for materials images
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propagated segmentation for materials images


This is the raw, uncensored research code for the forthcoming paper, "Combining global labeling and local relabeling for metallic image segmentation" in the SPIE Computational Imaging X conference. While written in C++, it is not idiomatic as it is translated from a previous MATLAB implementation for performance reasons.


This project requires

  • OpenCV

    Originally developed for <2.1, but now only runs on >2.1 unless you revert commit 08817c06835...

  • Andrew Delong and Olga Veksler's Multi-label optimization library (GCO) (GCO)

  • cvblobslib

    Developed with v6 in mind, which can be patched to work with OpenCV > 2.1.


The research nature of this code did not mandate a large amount of flexibility. You will have to manually configure

  • the makefile, to point to gco and cvblobslib, both compiled into statically-linked libraries

  • various configuration options in include/matscicut.h, which control various parameters and data locations

After configuration, simply running


should be sufficient to produce a matscicut executable that will do all processing (it presently allows the dilation amount and slice number to be specified as parameters).


For images, any type that OpenCV can read will be sufficient. For the labels (segmentations), these are found in plain text label files, where columns are delimited by spaces, and rows are newline delimited. These can be read into MATLAB with dlmread and the segmentation tools (seg2bmap.m readSeg.m) from the Berkeley Segmentation Benchmark can be used to display and process these segmentations.


This project is legacy code, and is completely unsupported. A new implementation consisting of some of the components of this project is already completed and (contingent upon its status) may be available upon request.

Copyright 2011 Jarrell Waggoner. All rights reserved.

Jarrell Waggoner