Local binary pattern (LBP) captures the local information of a texture effectively and thus plays a dominant role in many image processing applications. A LBP with (8, 1) or (8, 2) derives 2= 256 different patterns, to reduce this dimensionality uniform local binary patterns (ULBP) are derived and it has been proved that they capture fundamental properties of texture. The major disadvantage of ...

This paper proposes a texture analysis of the printed document based on Local Binary Pattern (LBP) descriptor for the application of printer identification. The LBP provides a statistical description of the pixels’ gray level differences within their neighborhoods. The occurrence histogram of local binary patterns is able to capture the document’s texture modifications by the distortion during ...

This paper addresses the challenging problem of the recognition and classification of textured surfaces given a single instance acquired under unknown pose, scale and illumination conditions. We propose a novel texture descriptor, the Adaptive Median Binary Pattern (AMBP) based on an adaptive analysis window of local patterns. The principal idea of the AMBP is to convert a small local image pat...

In this chapter, we propose two novel and curvature-free features: run-lengths of Local Binary Pattern (LBPruns) and Cloud Of Line Distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized images and gray scale images...

In this chapter, we propose two novel and curvature-free features: run-lengths of Local Binary Pattern (LBPruns) and Cloud Of Line Distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized images and gray scale images...

Recent developments in face analysis showed that local binary patterns (LBP) provide excellent results in representing faces. LBP is by definition a purely gray-scale invariant texture operator, codifying only the facial patterns while ignoring the magnitude of gray level differences (i.e. contrast). However, pattern information is independent of the gray scale, whereas contrast is not. On the ...

In this paper, we propose a new image feature based on spatial co-occurrence among micropatterns, where each micropattern is represented by a Local Binary Pattern (LBP). In conventional LBP-based features such as LBP histograms, all the LBPs of micropatterns in the image are packed into a single histogram. Doing so discards important information concerning spatial relations among the LBPs, even...

In this chapter, we propose two novel and curvature-free features: run-lengths of Local Binary Pattern (LBPruns) and Cloud Of Line Distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized images and gray scale images...

In this chapter, we propose two novel and curvature-free features: run-lengths of Local Binary Pattern (LBPruns) and Cloud Of Line Distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized images and gray scale images...

In this chapter, we propose two novel and curvature-free features: run-lengths of Local Binary Pattern (LBPruns) and Cloud Of Line Distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized images and gray scale images...