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Columnwise neighborhood operations
B = colfilt(A,[m n],block_type,fun)
B = colfilt(A,[m n],[mblock nblock],block_type,fun)
B = colfilt(A,'indexed',...)
B = colfilt(A,[m n],block_type,fun) processes the image A by rearranging each m-by-n block of A into a column of a temporary matrix, and then applying the function fun to this matrix. fun must be a function handle. Parameterizing Functions, in the MATLAB Mathematics documentation, explains how to provide additional parameters to the function fun. The function colfilt zero-pads A, if necessary.
Before calling fun, colfilt calls im2col to create the temporary matrix. After calling fun, colfilt rearranges the columns of the matrix back into m-by-n blocks using col2im.
block_type is a string that can have one of the values listed in this table.
Note: colfilt can perform operations similar to blockproc and nlfilter, but often executes much faster. |
Value | Description |
---|---|
'distinct' | Rearranges each m-by-n distinct block of A into a column in a temporary matrix, and then applies the function fun to this matrix. fun must return a matrix the same size as the temporary matrix. colfilt then rearranges the columns of the matrix returned by fun into m-by-n distinct blocks. |
'sliding' | Rearranges each m-by-n sliding neighborhood of A into a column in a temporary matrix, and then applies the function fun to this matrix. fun must return a row vector containing a single value for each column in the temporary matrix. (Column compression functions such as sum return the appropriate type of output.) colfilt then rearranges the vector returned by fun into a matrix the same size as A. |
B = colfilt(A,[m n],[mblock nblock],block_type,fun) processes the matrix A as above, but in blocks of size mblock-by-nblock to save memory. Note that using the [mblock nblock] argument does not change the result of the operation.
B = colfilt(A,'indexed',...) processes A as an indexed image, padding with 0's if the class of A is uint8 or uint16, or 1's if the class of A is double or single.
The input image A can be of any class supported by fun. The class of B depends on the class of the output from fun.
Set each output pixel to the mean value of the input pixel's 5-by-5 neighborhood.
I = imread('tire.tif'); I2 = uint8(colfilt(I,[5 5],'sliding',@mean)); figure subplot(1,2,1), imshow(I), title('Original Image') subplot(1,2,2), imshow(I2), title('Filtered Image')
blockproc | col2im | function_handle | im2col | nlfilter