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BNF Globalization Code (CVPR 2016)
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Ncut_9
binary_class_data
libs/vlfeat-0.9.18
multi_class_data
BNF_binary_class_RGB_affinity_demo.m
BNF_binary_class_edge_affinity_demo.m small changes Jun 20, 2016
BNF_multi_class_RGB_affinity_demo.m
BNF_multi_class_edge_affinity_demo.m small changes Jun 20, 2016
README.md
gen_supperpixel_info.m
read_img_rgb.m
vl_setupnn.m

README.md

Boundary Neural Fields Globalization

This is the code for Boundary Neural Fields globalization method. The technical report of the method can be found at http://arxiv.org/pdf/1511.02674v1.pdf

If you use this software please cite our CVPR 2016 paper:

@InProceedings{gberta_2016_CVPR,
author = {Gedas Bertasius and Jianbo Shi and Lorenzo Torresani},
title = {Semantic Segmentation with Boundary Neural Fields},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}

Installation

  1. VL Feat

    Compile the VL Feat library in the folder 'libs/'

  2. Normalized Cuts

    Compile Normalized Cuts code in the directory 'Ncuts_9/''

Usage

To use BNF method with the boundary based affinities, check out the files 'BNF_binary_class_edge_affinity_demo.m' and 'BNF_multi_class_edge_affinity_demo.m' for binary and multi class segmentations respectively. If the boundaries are not available, you can use the demos 'BNF_binary_class_RGB_affinity_demo.m' and 'BNF_multi_class_RGB_affinity_demo.m', which perform our globalization technique using the RGB color affinities.

Notes

This version of the code is slightly different than the one presented in the technical report.

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