A single-layer Random Forest model for pixel classification (image segmentation).
Switch branches/tags
Nothing to show
Clone or download
Cicconet Cicconet
Cicconet and Cicconet ...
Latest commit b700ea8 Feb 23, 2018
Permalink
Failed to load latest commit information.
docs ... Dec 4, 2017
LICENSE Initial commit Nov 17, 2017
ReadMe.txt ... Nov 20, 2017
circcentlikl.m ... Nov 17, 2017
circlikl.m ... Nov 17, 2017
derivatives.m ... Nov 17, 2017
figureQSS.m ... Nov 17, 2017
filterGauss2D.m ... Nov 17, 2017
filterLoG.m ... Nov 17, 2017
imClassify.m ... Nov 17, 2017
imageFeatures.m ... Nov 20, 2017
imreadGrayscaleDouble.m ... Dec 12, 2017
model.m ... Nov 17, 2017
padarrayXT.m ... Nov 17, 2017
parseLabelFolder.m ... Feb 22, 2018
pixelClassifier.m ... Feb 22, 2018
pixelClassifierTrain.m ... Nov 20, 2017
rfFeatAndLab.m ... Nov 17, 2017
rfTrain.m ... Nov 17, 2017
smorlet.m ... Nov 17, 2017
steerableDetector.m ... Nov 17, 2017
steerableDetector.mexa64 ... Nov 17, 2017
steerableDetector.mexmaci64 ... Nov 17, 2017
steerableDetector.mexw64 ... Nov 17, 2017

ReadMe.txt

A single-layer Random Forest model for pixel classification (image segmentation).


This code is based on
https://github.com/HMS-IDAC/MLRFS
and
https://github.com/HMS-IDAC/MLRFSwCF

The main differences are:
> Only one Random Forest layer is implemented. This makes the model simpler to understand and faster to train/test.
> More feature options are available, notably steerable and log filters. This makes it useful for a wider range or problems (e.g. filament and point source detection).
> Parallel processing is implemented, both during training and segmentation. This makes it significantly faster to train/execute.

The main scripts are:
pixelClassifierTrain, used to train the model, and
pixelClassifier, used to segment images after the model is trained.
See those files for details and parameters to set.

Labels/annotations can be created with ImageAnnotationBot, available at https://www.mathworks.com/matlabcentral/fileexchange/64719-imageannotationbot

A sample dataset for a running demo is available at https://www.dropbox.com/s/hl6jvwyea9vwh50/DataForPC.zip?dl=0

This code uses 2-D steerable filters for feature detection, developed by Francois Aguet, available at http://www.francoisaguet.net/software.html


Developed by:
Marcelo Cicconet
marceloc.net