Rank: 661 based on 168 downloads (last 30 days) and 3 files submitted
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Manohar

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11 Jul 2013 Reinhard Stain Normalization This approach maps the colour distribution of an over/under stained image to that of a well stained Author: Manohar medical imaging, stain normalization, histology slides, reinhard stain normal... 25 1
  • 5.0
5.0 | 1 rating
30 Oct 2012 Gabor Image Features Computation of Gabor Features - Mean Squared Energy, Mean Amplitude Author: Manohar texture analysis, gabor texture feature..., image processing 102 4
  • 5.0
5.0 | 2 ratings
29 Oct 2012 SMOTE (Synthetic Minority Over-Sampling Technique) The SMOTE function takes the feature vectors with dimension(r,n) and the target class with dimension Author: Manohar machine learning, class imbalance probl..., smote 41 2
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30 Jan 2014 Gabor Image Features Computation of Gabor Features - Mean Squared Energy, Mean Amplitude Author: Manohar Gore, Akshay

Adil
Use this for feature vector
http://www.mathworks.in/matlabcentral/fileexchange/44630-gabor-feature-extraction

14 Nov 2013 Gabor Image Features Computation of Gabor Features - Mean Squared Energy, Mean Amplitude Author: Manohar Adil

This code is very fine but can any body tell me how to get a feature vector from the two outputs...
suppose each image gives 40 "squared energy" and 40 "mean amplitude" now I want to create a [X by 230] vector to feed it to neural network where 230 are the images and X are features from each image....How can I do this

09 Aug 2013 Reinhard Stain Normalization This approach maps the colour distribution of an over/under stained image to that of a well stained Author: Manohar Sudaraka

Can you suggest some images for target?
I mean, I know it's application dependent, but say, for a particular stain (Giemsa, HE, etc.) it would be really awesome if you can provide a few sample images.
Keep up the good work :)

24 Jul 2013 SMOTE (Synthetic Minority Over-Sampling Technique) The SMOTE function takes the feature vectors with dimension(r,n) and the target class with dimension Author: Manohar Alhamdoosh, Monther

The code has a bug when removing. The last loop should be written as follows
for j = 1:length(original_mark)
neighbors = I(j, 2:4);
len = length(find(original_mark(neighbors) ~= original_mark(j,1)));
if(len >= 2)
if(original_mark(j,1) == classlabel)
train_incl(neighbors(original_mark(neighbors) ~= original_mark(j,1)),1) = 0;
else
train_incl(j,1) = 0;
end
end
end

The current code keeps changing the first 3 elements in train_incl.

05 Jul 2013 SMOTE (Synthetic Minority Over-Sampling Technique) The SMOTE function takes the feature vectors with dimension(r,n) and the target class with dimension Author: Manohar Mera, Carlos

Hi, when I used the SMOTE function, I get the next error. It was caused because an entry row of the I matrix is [0 0 0 0] (line 14). Why did this happen?.

Attempted to access P(0,:); index must be a positive integer or logical.

Error in SMOTE (line 24)
new_P=(1-th).*P(i,:) + th.*P(index,:);

Thanks and regards, Carlos

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