Frequency-tuned Salient Region Detection MATLAB代码出错修改方法

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% Copyright (c) 2009 Radhakrishna Achanta [EPFL]
% Contact: firstname.lastname@epfl.ch
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% Citation:
% @InProceedings{LCAV-CONF-2009-012,
%    author      = {Achanta, Radhakrishna and Hemami, Sheila and Estrada,
%                  Francisco and S黶strunk, Sabine},
%    booktitle   = {{IEEE} {I}nternational {C}onference on {C}omputer
%                  {V}ision and {P}attern {R}ecognition},
%    year        = 2009
% }
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% Please note that the saliency maps generated using this
% code may be slightly different from those of the paper.
% This seems to be because the RGB to Lab conversion is
% different from the one used for the results in the C++ code.
% The C++ code is available on the same page as this matlab
% code (http://ivrg.epfl.ch/supplementary_material/RK_CVPR09/index.html)
% One should preferably use the C++ as reference and use
% this matlab implementation mostly as proof of concept
% demo code.
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%
%
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% Read image and blur it with a 3x3 or 5x5 Gaussian filter
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img = imread(‘100\images\8.jpg‘);%Provide input image path
gfrgb = imfilter(img, fspecial(‘gaussian‘, 3, 3), ‘symmetric‘, ‘conv‘);
%symmetric图像大小通过镜像反射其边界来扩展 conv通过使用卷积来完成
%fspecial(‘gaussian‘, 3, 3)产生3*3高斯低通滤波器模板
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% Perform sRGB to CIE Lab color space conversion (using D65)
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%cform = makecform(‘srgb2lab‘, ‘whitepoint‘, whitepoint(‘d65‘));
cform = makecform(‘srgb2lab‘, ‘AdaptedWhitePoint‘, whitepoint(‘d65‘));
lab = applycform(gfrgb,cform);
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% Compute Lab average values (note that in the paper this
% average is found from the unblurred original image, but
% the results are quite similar)
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l = double(lab(:,:,1)); lm = mean(mean(l));
a = double(lab(:,:,2)); am = mean(mean(a));
b = double(lab(:,:,3)); bm = mean(mean(b));
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% Finally compute the saliency map and display it.
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sm = (l-lm).^2 + (a-am).^2 + (b-bm).^2;
imshow(sm,[]);
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