Sort blobs generated by cvBlobsLib by areas.

cvBlobsLib is an useful OpenCV 3rd party library to do blob analysis. However the blobs extracted are sorted based on their y coordinates and the author provided no interface to sort the blobs by other criteria such as area. To do so, you need to make some internal change. Add the following member function that sort the blobs based on their areas to the class CBlobResult.

    // sort blobs
    void SortBlobs()
    {
        struct myclass {
            bool operator() (CBlob* b1,CBlob* b2)
            {
                return (b1->GetArea() < b2->GetArea());
            }
        } blobComp;

        std::sort(m_blobs.begin(),m_blobs.end(),blobComp);
    }

If you want to sort the blobs based on their x coordinates, change the 7th line of above code to

return (b1->GetBoundingBox().x < b2->GetBoundingBox().x);

.

Optical flow in matlab

Implementation based on Horn’s optical flow algorithm.

function [Vx,Vy] = OpticalFlow(images,alpha,iterations)
%// Calculating optical flow of a sequence of images. 
%// images : 3D array that contains a sequence of images. size of images is (imageHeight, imageWidth, frame number)
%// alpha
%// iterations. 
[height,width,frames]=size(images);

%//initialzation of u and v
Vx = zeros(height,width);
Vy = zeros(height,width);

for k = 1:frames-1
    
    % //initialization of Ex Ey and Et 
    Ex = zeros(height-1,width-1,frames-1);
    Ey = zeros(height-1,width-1,frames-1);
    Et = zeros(height-1,width-1,frames-1);
    
    %//calculating Ex Ey and Et in frame k.
    for x = 2:width-1
        for y = 2:height-1
            Ex(y,x,k) = (images(y+1,x+1,k)-images(y+1,x,k)+images(y,x+1,k)...
                -images(y,x,k)+images(y+1,x+1,k+1)-images(y+1,x,k+1)...
                +images(y,x+1,k+1)-images(y,x,k+1))/4;
            
            Ey(y,x,k) = (images(y,x,k)-images(y+1,x,k)+images(y,x+1,k)...
                -images(y+1,x+1,k)+images(y,x,k+1)-images(y+1,x,k+1)...
                +images(y,x+1,k+1)-images(y+1,x+1,k+1))/4;
            
            Et(y,x,k) = (images(y+1,x,k+1)-images(y+1,x,k)+images(y,x,k+1)...
                -images(y,x,k)+images(y+1,x+1,k+1)-images(y+1,x+1,k)...
                +images(y,x+1,k+1)-images(y,x+1,k))/4;
        end
    end

    for nn = 1:iterations
        for x = 2:width-1
            for y = 2:height-1
                
                Vxbar = (Vx(y-1,x)+Vx(y,x+1)+Vx(y+1,x)+Vx(y,x-1))/6+...
                     (Vx(y-1,x-1)+Vx(y-1,x+1)+Vx(y+1,x+1)+Vx(y+1,x-1))/12;
                
                Vybar = (Vy(y-1,x)+Vy(y,x+1)+Vy(y+1,x)+Vy(y,x-1))/6+...
                    (Vy(y-1,x-1)+Vy(y-1,x+1)+Vy(y+1,x+1)+Vy(y+1,x-1))/12;

                %// chapter 12 of Horn's paper
                temp = (Ex(y,x,k)*Vxbar+Ey(y,x,k)*Vybar+Et(y,x,k))/(alpha^2 + Ex(y,x,k)^2 + Ey(y,x,k)^2);
                %// update u and v 
                Vx(y,x) = Vxbar-Ex(y,x,k)*temp;
                Vy(y,x) = Vybar-Ey(y,x,k)*temp;
            end
        end
    end
    
end

Fast calculation of integral image in matlab

After implementing a mex-files for calculating integral image in matlab, a friend of mine told me this can be easily done with the cumsum function in matlab.

intImage = cumsum(cumsum(double(img)),2);

SOM (Self-organizing map) code in matlab

Please visit my page to download complete code and training dataset.
https://sites.google.com/view/chi3x10/home

 

Below is the code of applying SOM on handwritten digits recongnition. It was implemented for a homework assignment in a course offered by professor Paul Gader. I used only ten handwritten digits images provided by Dr. Gader. Each image is resized to 30×30 and reshape to a 900×1 column vector. data in the code below is the training set. It’s a coolection of 900-dimensional column vectors.

Calculating principal components of data with VERY HIGH dimensionality

Suppose we have a set of data X with dimensionality D and total number of data is N. You might encounter insufficient memory problem when D is a large number or simply waste too much time calculating those redundant components when D >> N. Using singular value decomposition can prevent those problems.

From singular value decomposition, we know that a D-by-N matrix X can be decomposed as , where U is composed of the eigenvectors of and of the size D-by-D and V is composed of the eigenvectors of and of size N-by-N. Because is an identity matrix(column vectors in V are orthonormal), the first equation can be written as . From the question, the first m (m<=N) columns of matrix U are actually the principle components of X. We can actually get the first effect m columns of U without actually calculating . Here is how it works.

Saving google images search results into your own computer.

I need tons of images for my computer vision research. The best way to find images nowadays is using google images search. However, saving image one by one to is just too much work. Luckily, a program called WebImageGrab can do the saving part for us. All you need to do is to type in the key word and this program will store all the images returned by google into your local hard drive.