Results 221 to 230 of about 126,496 (264)

Local Binary Patterns of Segments of a Binary Object for Shape Analysis

Journal of Mathematical Imaging and Vision, 2022
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Ratnesh Kumar, Kalyani Mali
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Soft local binary patterns

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2015
Local Binary Pattern (LBP) is known as one of the most effective local descriptors for image recognition. It is invariant to monotonic gray-scale changes of the image. Local neighborhood information is gathered for each pixel of the image, and a binary code is generated by comparing its value with the value of the center pixel.
Ran Li, Xuezhen Li, Takio Kurita
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Similarity assessment with local binary patterns

2012 20th Signal Processing and Communications Applications Conference (SIU), 2012
Identification from human face plays an important role in social interaction, such as recognition and security. Thus facial information processing is an active research area in pattern recognition. The similarity of a child's face to parent faces is evaluated in this paper.
Vasif V. Nabiyev, Beste Gencturk
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Number Local binary pattern: An Extended Local Binary Pattern

2011 International Conference on Wavelet Analysis and Pattern Recognition, 2011
An extension of local binary pattern, named Number Local binary pattern (NLBP), is presented for texture analysis. First, the method divides the patterns into uniform and non-uniform according to the uniform measure. Second, the non-uniform pattern is further divided into different groups based on the numbers of ‘1’ bits and ‘0’ bits.
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Local Binary Patterns for Gender Classification

Proceedings of the 7th International Conference on Software Engineering and New Technologies, 2018
Several approaches for gender of handwriting are proposed an appearance feature-based approach. In this paper we present a comparative study to evaluate effectiveness of different Local Binary Patterns methodologies in characterizing gender from handwriting. We investigate different local binary patterns (LBP) parameters with/without preprocessing step
Faycel Abbas, Abdeljalil Gattal, Mohamed Ridda Laouar, Kamel Saoudi, Ismail Hadjadj   +4 more
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Discriminative local binary pattern

Machine Vision and Applications, 2016
Local binary pattern (LBP) is widely used to extract image features as well as motion features in various visual recognition tasks. LBP is formulated in quite a simple form and thus enables us to extract effective features with a low computational cost.
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Mixed co-occurrence of local binary patterns and Hamming-distance-based local binary patterns

Information Sciences, 2018
Abstract Local binary patterns (LBP) have powerful discriminative capabilities. However, traditional methods with LBP histograms cannot capture spatial structures of LBP codes. To extract the spatial structures of an LBP code map, we compute and encode the Hamming distances between LBP codes of a center point and its neighbors on the LBP code map to ...
Feiniu Yuan, Xue Xia 0005, Jinting Shi
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Face Recognition with Local Binary Patterns

2004
In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face images. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face
Timo Ahonen, Abdenour Hadid, Matti Pietikäinen   +2 more
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Weight-Optimal Local Binary Patterns

2015
In this work, we have proposed a learning paradigm for obtaining weight-optimal local binary patterns (WoLBP). We first re-formulate the LBP problem into matrix multiplication with all the bitmaps flattened and then resort to the Fisher ratio criterion for obtaining the optimal weight matrix for LBP encoding.
Felix Juefei-Xu, Marios Savvides
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A Bayesian Local Binary Pattern texture descriptor

2008 19th International Conference on Pattern Recognition, 2008
In this paper, a Bayesian LBP operator is proposed. This operator is formulated in a novel filtering, labeling and statistic (FLS) framework for texture descriptors. In the framework, the local labeling procedure, which is a part of many popular descriptors such as LBP, SIFT and VZ, can be modeled as a probability and optimization process. This enables
He Chu, Pietikäinen Matti, Ahonen Timo
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