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Simple linear iterative clustering (SLIC) in a region of interest
This code demonstrates the adaption of SLIC for a defined region of interest. The main contribution is in the placement of seed points within the ROI.
The code is a modification of the SLIC implementation provided by Scikit-image (http://scikit-image.org/)
An online demo is available at: http://maskslic.birving.com/index
An outline of the method is available at: http://arxiv.org/abs/1606.09518
Figure 1: An example region and the automatically placed seed points using this method.
Figure 2: The final superpixel regions within the ROI
Install dependencies
pip install -r requirements.txt
build cython code
python setup.py build_ext --inplace
run example
python run_example.py
Using a python virtualenv is reccommended on linux to get the latest versions of the dependencies.
This code is still a work in progress.
FAQs
Simple linear iterative clustering (SLIC) in a region of interest (ROI)
We found that maskslic demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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