=========================================================================== Multi-scale Stochastic Regional Texture Segmentation v_1.0 (2015-06-23) =========================================================================== Written by Rafael Sachett Medeiros <rsmedeiros@inf.ufrgs.br> Jacob Scharcanski <jacobs@inf.ufrgs.br> Alexander Wong <a28wong@uwaterloo.ca> =========================================================================== Content: This algorithm was introduced in the paper: Medeiros, R. S., J. Scharcanski, and A. Wong. "Image Segmentation via Multi-scale Stochastic Regional Texture Appearance Models." Computer Vision and Image Understanding (2016). =========================================================================== INSTALLATION: The matlab codes are a standalone implementation, and no instalation is needed. To use the mex (C/C++) implementation of the Stochastic Region Merging you must have the GSL and BLAS libraries installed on your computer. GSL: https://www.gnu.org/software/gsl/ BLAS: http://www.netlib.org/blas/ =========================================================================== USAGE: Run the 'demo.m' for segmenting the images in the "./Images/test/" folder. Output will be a "*_reg" file (with the boundaries marked over the image) and a "*_seg" file (with the segmentation map), both saved in "./Images/output/". See the file 'demo.m' for instructions on how to configure the software. =========================================================================== FILES: BiSS - Multi-scale decomposition library demo.m - Test script for running this software Images - Some test Images README.txt - This file SRMlib - Stochastic Region Merging Library STRlib - Stochastic Texture Representation library =========================================================================== REFERENCES: If you use this software please refer to the works: Medeiros, R. S., J. Scharcanski, and A. Wong. "Image Segmentation via Multi-scale Stochastic Regional Texture Appearance Models." Computer Vision and Image Understanding (2015). Medeiros, R.S.; Scharcanski, J.; Wong, A., "Natural scene segmentation based on a stochastic texture region merging approach," ICASSP 2013.