Seeded Laplacian Version 2
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After_optimization
Before_optimization
MatlabFns
code/piotr_toolbox
README.md
applythresholdtoimage.m
build.m
calculateJaccardIndex.m
calculate_accuracy_measures2.m
calculate_eigenvector.m
colorandspatialaffinity.m
computeDistances.m
computelocationvector.m
config_bsd100.txt
config_geo.txt
config_wzn1.txt
config_wzn2.txt
config_wzn_horses.txt
dist2.m
dist_mat.m
eigenfunctions.m
fuf.m
getConnectedDistanceVector.m
getImageLabColor.m
get_channels.m
getconnectedcomponent.m
marker2bwdist.m
markers2samples.m
mex_compute_all_descriptors.cpp
mex_compute_all_descriptors.mexglx
mex_compute_all_descriptors.mexw64
mex_compute_descriptor.cpp
mex_compute_descriptor.mexglx
mex_compute_descriptor.mexw64
negativemarker2samples.m
normalize.m
numericalEigenFunctions.m
percentile.m
perform_front_propagation_2d_color.mexmaci64
perform_front_propagation_2d_color.mexw64
positivemarker2samples.m
processf_efunc.m
saveimagetopath.m
scribble_image.m
scribble_script.m
smoothImage.m

README.md

Seeded Laplacian Version 2

This MATLAB code is the latest version of Seeded Laplacian approach for interactive image segmentation. This version doesn't include the robot-user evaluation part, we plan to release that soon. It is important to note that this code needs a lot of refactoring and style improvement. It is just pushed for those interested in verifying its results. I plan to push further commit with better documentation and style.

Datasets

This code works with the collection of annotated datasets of images we prepared. This collection comprise of

  • Geodesic Star Convexity
  • Weizmann Horses
  • Weizmann Single Object
  • Weizmann Two objects
  • BSD 100

You can download the collected annotated datasets from this link. For copyright purpose, we don't distribute the original or ground-truth images. If you can't find these online, you can contact us to help you with that.

Results

Method Geodesic Horses Single Two BSD 100
BJ 0.49 ± 0.26 0.60 ± 0.23 0.66 ± 0.24 0.48 ± 0.27 0.53 ± 0.25
RW 0.53 ± 0.21 0.55 ± 0.15 0.42 ± 0.26 0.63 ± 0.25 0.49 ± 0.23
PP 0.59 ± 0.25 0.60 ± 0.24 0.68 ± 0.23 0.71 ± 0.25 0.59 ± 0.24
RW 0.61 ± 0.25 0.57 ± 0.22 0.69 ± 0.23 0.70 ± 0.25 0.57 ± 0.25
GSC 0.61 ± 0.24 0.55 ± 0.21 0.67 ± 0.22 0.68 ± 0.25 0.56 ± 0.25
ESC 0.56 ± 0.16 0.51 ± 0.11 0.48 ± 0.16 0.61 ± 0.20 0.52 ± 0.15
SP-IG 0.59 ± 0.22 0.48 ± 0.20 0.57 ± 0.24 0.60 ± 0.25 0.55 ± 0.23
SP-SIG 0.62 ± 0.17 0.57 ± 0.12 0.57 ± 0.18 0.70 ± 0.18 0.59 ± 0.15
SL 0.69 ± 0.17 0.63 ± 0.15 0.72 ± 0.17 0.75 ± 0.17 0.64 ± 0.17

Setup

  • Download the latest push.
  • Run the scribble_script MATLAB script file

For help, please contact ahmdtaha [@] cs dot umd dot edu

Contributor list

  1. Ahmed Taha
  2. Marwan Torki

License

Copyright (c) 2016, Ahmed Taha (ahmdtaha [@] cs dot umd dot edu) All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  • Re distributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
  • Re distributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

Scribble Segmentation Call sequence

  • scribble_script
    • scribble_image {for each image} , return Jaccard Index
      • colorandspatialaffinity {calculate features and eFunc} , return filled Image and erroded Image.
      • Currently Feature Vector contains {RGB Affinity_Distance Affinity_LAB Affinity_IC Affinity (neg - pos) ,LAB,RGB}
        • processf_efunc(efunc) , return filled Image
        • if input percentile = -1 then processf_efunc calculate min_percentile plot else processf_efunc uses the input parameter