CODEWITHSUNDEEP
matlabimage-processingcomputer-vision
Jordon Birk
Jordon Birk

Reputation: 490

Matlab im2col function

This is a bit more of a general question, but, no matter how many times I read the description of MATLAB's im2col function, I cannot fully understand it. I need it for the computational efficiency because MATLAB is awful with nested for loops. Here's what I'm attempting to do, but using nested for loops:

 [TRIMMED]=TM_FILTER(IMAGE, FILTER_SIZE, PERCENT)
    Takes a 2-D array and returns the array, filtered with a
    square trimed mean filter with length/width equal to FILTER_SIZE and percent equal to PERCENT.

    %}
    function [trimmed]=tm_filter(image, filter_size, percent)
    if rem(filter_size, 2)==0                            %make sure filter has a center pixel
        error('filter size must be odd numbered');       %error and return if number is odd
        return 
    end
    if percent > 100 || percent < 0
        error('Percentage must be ? [0, 100]');
        return
    end

    [rows, columns]=size(image);              %figure out pixels needed
    n=(filter_size-1)/2;                      %n is pixel distance from center pixel to boundaries  
    padded=(padarray(image, [n,n],128));      %padding on boundaries so center pixel always has neighborhood

for i=1+n:rows                            %rows from first non-padded entry to last nonpadded entry
    for j=1+n:columns                     %colums from first non-padded entry to last nonpadded entry
    subimage=padded(i-n:i+n,j-n:j+n);     %neighborhood same size as filter
    average=trimmean(trimmean(subimage, percent), percent);         %computes trimmed mean of neighborhood as trimmed mean of vector of trimmed means
    trimmed(i-n, j-n)=average;         %stores averaged pixel in new array
    end
end
trimmed=uint8(trimmed);             %converts image to gray levels from 0-255

Upvotes: 2

Views: 4065

Answers (2)

Floris
Floris

Reputation: 46415

Here is the code you want: note the entire nested loop was replaced with a single statement.

 [TRIMMED]=TM_FILTER(IMAGE, FILTER_SIZE, PERCENT)
    Takes a 2-D array and returns the array, filtered with a
    square trimed mean filter with length/width equal to FILTER_SIZE and percent equal to PERCENT.

    %}
    function [trimmed]=tm_filter(image, filter_size, percent)
    if rem(filter_size, 2)==0                            %make sure filter has a center pixel
        error('filter size must be odd numbered');       %error and return if number is odd
        return 
    end
    if percent > 100 || percent < 0
        error('Percentage must be ? [0, 100]');
        return
    end

    trimmed = (uint8)trimmean(im2col(image, filter_size), percent);

Explanation:

the im2col function turns each region of filter_size into a column. Your trimmean function can then operate on each of the regions (columns) in a single operation - much more efficient than extracting each shape in turn. Also note this requires only a single application of trimmean - in your original you first do it on the columns, then again on the rows, which will actually cause a more severe trim than I think you intended (exclude 50% first time, then 50% again - feels like excluding 75%. Not exactly true but you get my point). Also you will find that changing the order of operations (row, then column vs column, then row) would change the result because the filter is nonlinear.

For example

im = reshape(1:9, [3 3]);
disp(im2col(im,[2 2])

results in

1  2  4  5
2  3  5  6
4  5  7  8
5  6  8  9

since you took each of the 4 possible blocks of 2x2 from this matrix:

1  4  7
2  5  8
3  6  9

and turned them into columns

Note - with this technique (applied to the unpadded image) you do lose some pixels on the edge; your method added some padding so that every pixel (even ones on the edge) has a complete neighborhood, and as such the filter returns an image that is the same size as the original (but it's not clear what the effect of padding/filtering will be near the margin, and especially the corner: you have almost 75% percent of pixels fixed at 128 and that is likely to dominate the behavior in the corner).

Upvotes: 3

Shai
Shai

Reputation: 114866

  1. why im2col? why not nlfilter?

    >> trimmed = nlfilter( image, [filter_size filter_size],...
                       @(x) treimmean( trimmean(x, percent), percent ) );

  2. Are you sure you process the entire image?
    i and j only goes up to rows and columns respectively. However, when you update trimmed you access i-n and j-n. What about the last n rows and columns?

  3. Why do you apply trimmean twice for each block? Isn't it more appropriate to process the block at once, as in trimmean( x(:), percent)?
    I believe the results of trimmean( trimmean(x, percent), percent) will be different than those of trimmean( x(:), percent). Have you give it a thought?

  4. A small remark, it is best not to use i and j as variable names in matlab.

Upvotes: 2

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