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This repository contains the source code for the method described in

"Oriented Edge Forests for Boundary Detection".Sam Hallman, Charless Fowlkes. CVPR, June 2015

The system is implemented in MATLAB. On a 480-by-320 image, the detector shouldrun in ~2 seconds on an 8-core machine. Development was done on Linux andpre-compiled MEX binaries for Linux are included.

To use this software, you need to have Piotr Dollar's very usefulImage & Video Matlab Toolbox installed.

You can download a pre-trained model athttp://www.ics.uci.edu/~shallman/oef/modelCvpr.mat.The file is 98 MB, but swells to 1.1 GB when loaded into memory.To train a model yourself, you'll need to download theBSDS500 dataset.

Seedemo.m for usage examples.

To train a reasonably good detector quickly,

% requires ~5GB of RAM and <4 min/treemodel = train('nPos',5e5, 'nNeg',5e5, 'nTrees',8, ...  'useParfor',1, 'calibrate',0, 'bsdsDir','/path/to/bsds/');

To train the model from the CVPR paper, just use the default settings:

% requires ~19GB of RAM and ~15 min/treemodel = train('bsdsDir','/path/to/bsds/');

This trains 24 trees by default, because that is originally how I derived thenumbers shown in the paper. But 24 trees is probably overkill, and I would betthat you'd get the same results with 12 trees.

Many files were built on top of files from theSketch Tokens andStructuredForest packages. I also make use of the edge linking files from Peter Kovesi'sMATLAB and Octave Functions for Computer Vision and Image Processing page.