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Creates histogram and map of Linear Binary Patterns (with radius 1 and size 8) of provided image.

Name Type Description
inImage Image Mono-channel image.
inRoi Region* Region of Interest.
inPatternType LinearBinaryPatternType Type of LBP to produce.
outPatternHistogram Histogram Histogram of LBP codes found in inImage.
outTextureImage Image LBP map of input image.


For input inImage only pixel formats are supported: 1⨯uint8.

Read more about pixel formats in Image documentation.


The LPB histogram (treated as a 256 dimensional feature vector) is a texture visual descriptor useful for texture classification. It is obtained by calculating a pattern for each pixel, and then producing a histogram from all these numbers. The pattern is a 8-bit array (coded in uint8) with results of comparisons of the center pixel value to all its neighbours.
Following types of LPB can be calculated:

  • Plain
  • RotationInvariant - all patterns (and thus histogram bins) that can be obtained by binary shifts from each other are merged into one
  • Uniform - non-uniform patterns (these containing more than two 01 or 10 transitions - e.g. 00110010 has 4) are merged into one bin
  • UniformRotationInvariant - apply both RotationInvariant and Uniform operations

More information can be found at


Source image with following ROIs defined:
top-left fur (reference), top-right fur, and bottom nose.

LBP histograms calculated for top-left and top-right regions (different parts of fur)
The mean-squared distance between these feature vectors (histograms) is 264,6.

LBP histograms calculated for top-left region and bottom region (nose)
The mean-squared distance between these feature vectors is 1472,3.

Complexity Level

This filter is available on Basic Complexity Level.