applylut

Neighborhood operations on binary images using lookup tables

applylut is not recommended. Use bwlookup instead.

Description

example

A = applylut(BW,lut) performs a 2-by-2 or 3-by-3 neighborhood operation on binary image BW by using a lookup table, lut. The lookup table consists of the output values for all possible 2-by-2 or 3-by-3 neighborhoods.

Examples

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Create the LUT.

 lutfun = @(x)(sum(x(:))==4);
 lut    = makelut(lutfun,2);

Read image into the workspace and then apply the LUT to the image. An output pixel is on only if all four of the input pixel's neighborhood pixels are on .

 BW1    = imread('text.png');
 BW2    = applylut(BW1,lut);

Show the original image and the eroded image.

 figure, imshow(BW1);

 figure, imshow(BW2);

Input Arguments

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Input image, specified as a 2-D binary image. For numeric input, any nonzero pixels are considered to be 1 (true).

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

Lookup table of output pixel values, specified as a 16- or 512-element vector as returned by makelut.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

Output Arguments

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Output image, returned as a grayscale or binary image whose distribution of pixel values are determined by the content of the lookup table, lut. The output image J is the same size as the input image I.

  • If all elements of lut are 0 or 1, then A has data type logical.

  • If all elements of lut are integers between 0 and 255, then A has data type uint8.

  • For all other cases, A has data type double.

Data Types: double | uint8 | logical

Algorithms

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applylut performs a neighborhood operation on a binary image by producing a matrix of indices into lut, and then replacing the indices with the actual values in lut. The specific algorithm used depends on whether you use 2-by-2 or 3-by-3 neighborhoods.

2-by-2 Neighborhoods

For 2-by-2 neighborhoods, length(lut) is 16. There are four pixels in each neighborhood, and two possible states for each pixel, so the total number of permutations is 24 = 16.

To produce the matrix of indices, applylut convolves the binary image BW with this matrix.

8     2
4     1

The resulting convolution contains integer values in the range [0, 15]. applylut uses the central part of the convolution, of the same size as BW, and adds 1 to each value to shift the range to [1, 16]. The function then constructs A by replacing the values in the cells of the index matrix with the values in lut that the indices point to.

3-by-3 Neighborhoods

For 3-by-3 neighborhoods, length(lut) is 512. There are nine pixels in each neighborhood, and two possible states for each pixel, so the total number of permutations is 29 = 512.

To produce the matrix of indices, applylut convolves the binary image BW with this matrix.

256    32     4
128    16     2
 64     8     1

The resulting convolution contains integer values in the range [0, 511]. applylut uses the central part of the convolution, of the same size as BW, and adds 1 to each value to shift the range to [1, 512]. It then constructs A by replacing the values in the cells of the index matrix with the values in lut that the indices point to.

Compatibility Considerations

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Not recommended starting in R2012b

See Also

Introduced before R2006a