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The software is made publicly available for the purpose of advancing research in computer vision and related machine learning areas. It may be modified and redistributed under the terms of the **

**User program**- example.m : an example on ncuts for image segmentation
**Graph cuts**- imncut.m : master program on ncuts for image segmentation
- cncut.m : constrained ncuts with attraction, repulsion and regularization
- getbinsol.m : get a discrete partitioning from eigensolutions
- pargrp.m : generate a constraint matrix from partial grouping cues
- barqpqz.c : projection formula used in cncut
**Image feature extraction**- ic.c : compute intervening contours
- quadeg.m : edge extractions through quadrature filters
- make_filterbank_even2.m : make even-phase filters
- make_filterbank_odd2.m : make odd-phase filters
- doog1.m : 1D difference of Gaussian
- doog2.m : 2D difference of Gaussian
- fft_filt_2.m : filtering by FFT
- gaussian.m : Gaussian function
**Sampling and speed up**- imnb.c : pixel pairs in a local window
- csparse.c : generate a sparse matrix from C-index representation
- spmd.c : sparse matrix times diagonal matrices
- parmatV.c : binary partition matrix times a real matrix
**Image**- 135069.jpg : from Berkeley image dataset, 160 x 240
**Notes**- These programs incorporate new developments on spectral graph partitioning as described in the following thesis:
- Computational Models of Perceptual Organization
- Stella X. Yu, May 2003, Carnegie Mellon University
- except Chapter 4 where Hermitian matrices are used.
- However, they can be extended by modifying related c functions to deal with imaginary number multiplications.