  In many image processing applications, such as segmentation and
classification, the selection of robust features descriptors is crucial to
improve the discrimination capabilities in real world scenarios. In particular,
it is well known that image textures constitute power visual cues for feature
extraction and classification. In the past few years the local binary pattern
(LBP) approach, a texture descriptor method proposed by Ojala et al., has
gained increased acceptance due to its computational simplicity and more
importantly for encoding a powerful signature for describing textures. However,
the original algorithm presents some limitations such as noise sensitivity and
its lack of rotational invariance which have led to many proposals or
extensions in order to overcome such limitations. In this paper we performed a
quantitative study of the Ojala's original LBP proposal together with other
recently proposed LBP extensions in the presence of rotational, illumination
and noisy changes. In the experiments we have considered two different
databases: Brodatz and CUReT for different sizes of LBP masks. Experimental
results demonstrated the effectiveness and robustness of the described texture
descriptors for images that are subjected to geometric or radiometric changes.
