hms-idac / pixelclassifier Goto Github PK
View Code? Open in Web Editor NEWA single-layer Random Forest model for pixel classification (image segmentation).
Home Page: https://hms-idac.github.io/PixelClassifier/
License: MIT License
A single-layer Random Forest model for pixel classification (image segmentation).
Home Page: https://hms-idac.github.io/PixelClassifier/
License: MIT License
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
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