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The proposed descriptor is compared with its available state of the art counterparts. Numerous and extensive experiments on these datasets with different kinds of noises indicate this newly developed descriptorвЂ™s efficiency, with or without incremental white Gaussian and Gaussian blur noises.

To evaluate the functionality of DTLBP, four known datasets including Outex (TC10), CUReT, UIUC and UMD are tested. The DTLBP is able to be combined with other descriptors like completed LBP (CLBP) and local ternary pattern (LTP) which improves their functionality against noise. Applying this type of thresholding in addition to reducing noise, due to using the information of neighboring pixels with bigger radii, increases efficiency in extracting features. To make LBP robust against noise, a directional thresholded LBP (DTLBP) is developed in this article which applies the directional neighboring pixels average values for thresholding. Despite its outstanding abilities in revealing textural properties of image, it is sensitive to noise, due to its thresholding mechanism. Local binary pattern (LBP) is a multi-applicable texture descriptor applied in machine vision.

Noise dexture