Asked by Michal Kvasnicka
on 27 Sep 2018

I need to effectively eliminate consecutive regions in vector "a" or better in rows/columns of matrix "A" with length of separate ones regions greater than positive integer N <= length(A):

See following example:

N = 2 % separate consecutive regions with length > 2 are zeroed

a = [0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1]

a_elim = [0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1]

or 2D case:

N = 2

A = [1 0 1 …

1 1 0 …

1 1 0 …

0 0 1 …

1 1 1]

% elimination over columns

A_elim= 0 0 1

0 1 0

0 1 0

0 0 1

1 1 1

% elimination over rows

A_elim= 1 0 1

1 1 0

1 1 0

0 0 1

0 0 0

I am looking for effective vectorized function performing this task for size(A) ~ [100000, 1000] (over columns case).

Answer by Matt J
on 27 Sep 2018

Edited by Matt J
on 27 Sep 2018

Accepted Answer

e=ones(N+1,1);

if mod(N,2) %even mask

mask=~conv2(conv2(A,e,'valid')>=N+1 ,[zeros(N,1);e])>0;

mask=mask(N+1:end,:);

else %odd mask

mask=~(conv2( conv2(A,e,'same')>=N+1, e,'same')>0);

end

A_elim=A.*mask;

Bruno Luong
on 27 Sep 2018

The above code is not working for N odd (e.g. N=1)

Matt J
on 27 Sep 2018

I've modified it to handle odd N.

Michal Kvasnicka
on 29 Sep 2018

Hi Matt … now your solution works very well. Is faster and significantly less memory consuming than Bruno's code. Moreover, the "mask" is very useful to know.

Thanks!!!

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Answer by Bruno Luong
on 27 Sep 2018

Edited by Bruno Luong
on 27 Sep 2018

You could use Huffman encoding (there might be some on the FEX), but the idea is similar to this direct code:

N = 2

A = [1 0 1;

1 1 0;

1 1 0;

0 0 1;

1 1 1];

% Engine for working along the column (1st dimension)

[m,n] = size(A);

z = zeros(1,n);

Apad = [z; A; z];

d = diff(Apad,1,1);

[i1,j1] = find(d==1);

[i0,j0] = find(d==-1);

lgt = i0-i1;

keep1 = lgt <= N;

keep0 = keep1 & i0 <= m;

i1 = [i1,j1];

i0 = [i0,j0];

C1 = accumarray(i1(keep1,:),1,[m n]);

C0 = accumarray(i0(keep0,:),-1,[m n]);

Aclean = cumsum(C1+C0,1);

Aclean

If you want to filter along the 2nd dimension, transpose A, apply the above, then transpose the result Aclean.

Michal Kvasnicka
on 27 Sep 2018

Does it mean, that use of Huffman encoding brings some performance improvement?

Bruno Luong
on 27 Sep 2018

It depends how it's implemented. If it's a C-MEX file might be, pure MATLAB Huffman, no chance.

Otherwise my code is probably quite fast (but it creates few big intermediate arrays, you might add "clear...) while the code is running when an array is finished to be used).

Why not test yourself with different methods the link you have found?

Michal Kvasnicka
on 27 Sep 2018

Yes, you are right, the pure MATLAB Huffman encoding is not quite fast. I will test all options. Anyway, your code looks as very good method.

Thanks!

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Answer by Michal Kvasnicka
on 27 Sep 2018

Edited by Michal Kvasnicka
on 27 Sep 2018

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