Recently, we have developed a nonparametric approach to texture analysis based on simple spatial operators like local binary patterns and signed gray level differences. Very good performance has been obtained in various texture classification and segmentation problems. This paper overviews our approach and presents examples to demonstrate its efficiency. Our results suggest that complementary f...

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In this paper, a new algorithm meant for object tracking application is proposed using local extrema patterns (LEP) and color features. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray level values. The proposed method differs from the existing LBP in a manner that it extracts the edge information based on l...

Previously, we proposed an efficient algorithm using vector quantization (VQ) histogram for facial image recognition in low-frequency DCT domains. In this paper, we newly utilize Local Binary Pattern (LBP) histogram in spatial domain. These two histograms, which contain both spatial and frequency domain information of a facial image, are utilized as a very effective personal feature. Publicly a...

Feature extraction plays an important role in face recognition. Based on local binary patterns (LBP), we propose a novel face representation method which obtains histograms of semantic pixel sets based LBP (spsLBP) with a robust code voting (rcv). By clustering according the semantic pixel relations before the histogram estimation, the spsLBP makes better use of the spatial information over the...

Three different mechanisms are presented to allow for the representation of 3D surfaces in such a way that key features are retained while at the same time ensuring compatibility with prediction (classification) techniques. The application domain is sheet metal forming. The representations are designed to capture the nature of the surface to be manufactured and predict deformations, known as “s...

This paper presents a novel automatic face recognition approach based on local binary patterns (LBP). LBP descriptor considers a local neighbourhood of a pixel to compute the features. This method is not very robust to handle image noise, variances and different illumination conditions. In this paper, we address these issues and extend the original LBP operator by considering more pixels and di...

We propose a novel binary feature for object detection encoding local neighbor patterns of different sizes and locations. Each region pair of the proposed feature is selected by RealAdaBoost algorithm with a penalty term on the structure diversity. As a result, useful features that are good at describing specific objects will be chosen to build the classifier. Moreover, the encoding scheme is a...

In this paper, a novel local texture descriptor termed as Local Block-Difference Pattern (LBDP) is proposed. In conventional LBP, sensitive to intensity change problem will drastically affect the performance due to its simple pixel value comparison mechanism. Different from LBP, the proposed LBDP describes the local textures from a pixel to a block for decreasing the impacts resulting from inte...