This code implements the simulation results reported in:
M. W. Spratling (2013) Image Segmentation Using a Sparse Coding Model of Cortical Area V1. IEEE Transactions on Image Processing, 22(4):1631-43.
Please cite this paper if this code is used in, or to motivate, any publications.
This code requires MATLAB and the Image Processing Toolbox. It was tested with MATLAB Version 7.8 (R2009a) and Image Processing Toolbox Version 6.3 (R2009a).
Simulations will run much faster if you install the MATLAB function convnfft written by Bruno Luong, which is available here: http://www.mathworks.com/matlabcentral/fileexchange/24504-fft-based-convolution
To apply the PC/BC model to an image, do the following:
%LOAD THE IMAGE I=im2single(imread('file.pgm')); %PRE-PROCESS IMAGE TO GENERATE INPUT TO V1 MODEL X=preprocess_V1_input(I); %APPLY PC/BC OF V1 TO THE IMAGE [y,s,r]=v1_edge_detection(X);
The outputs are a cell array, y, containing the activations of the prediction neurons; a scalar, s, giving Hoyer's sparsity index for the prediction neuron activations; and, a reconstruction, r, of the boundary edges in the image.
To apply the algorithm to the BSDS300 dataset, you will need to install the images and code for performing this benchmark, both of which are available here: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/
Function berkeley_benchmark.m (with the paths edited appropriately) will apply the PC/BC algorithm to this benchmark. Alternatively, to speed things up benchmarking can be performed in parallel across multiple CPU cores using the function berkeley_benchmark_multicore.m together with the code written by Markus Buehren, which is available here: http://www.mathworks.com/matlabcentral/fileexchange/13775-multicore-parallel-processing-on-multiple-cores