Journal ArticleDOI
Noise-Resistant Local Binary Pattern With an Embedded Error-Correction Mechanism
TLDR
A noise-resistant LBP (NRLBP) is proposed to preserve the image local structures in presence of noise and an error-correction mechanism to recover the distorted image patterns is developed.Abstract:
Local binary pattern (LBP) is sensitive to noise. Local ternary pattern (LTP) partially solves this problem. Both LBP and LTP, however, treat the corrupted image patterns as they are. In view of this, we propose a noise-resistant LBP (NRLBP) to preserve the image local structures in presence of noise. The small pixel difference is vulnerable to noise. Thus, we encode it as an uncertain state first, and then determine its value based on the other bits of the LBP code. It is widely accepted that most of the image local structures are represented by uniform codes and noise patterns most likely fall into the non-uniform codes. Therefore, we assign the value of an uncertain bit hence as to form possible uniform codes. Thus, we develop an error-correction mechanism to recover the distorted image patterns. In addition, we find that some image patterns such as lines are not captured in uniform codes. Those line patterns may appear less frequently than uniform codes, but they represent a set of important local primitives for pattern recognition. Thus, we propose an extended noise-resistant LBP (ENRLBP) to capture line patterns. The proposed NRLBP and ENRLBP are more resistant to noise compared with LBP, LTP, and many other variants. On various applications, the proposed NRLBP and ENRLBP demonstrate superior performance to LBP/LTP variants.read more
Citations
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Journal ArticleDOI
Local binary features for texture classification
TL;DR: A large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets.
Journal ArticleDOI
Median Robust Extended Local Binary Pattern for Texture Classification
TL;DR: A comprehensive evaluation on benchmark data sets reveals MRELBP’s high performance—robust to gray scale variations, rotation changes and noise—but at a low computational cost.
Journal ArticleDOI
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
TL;DR: More than 250 major publications are cited in this survey covering different aspects of the research, including benchmark datasets and state-of-the-art results as discussed by the authors, in retrospect of what has been achieved so far and open challenges and directions for future research.
Journal ArticleDOI
LBP-Based Edge-Texture Features for Object Recognition
TL;DR: DRLBP and DRLTP solve the problem of discrimination between a bright object against a dark background and vice-versa inherent in LBP and LTP and retain contrast information necessary for proper representation of object contours that LBP, LTP, and RLBP discard.
Proceedings ArticleDOI
Median robust extended local binary pattern for texture classification
TL;DR: A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost.
References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Journal ArticleDOI
A comparative study of texture measures with classification based on featured distributions
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.
Journal ArticleDOI
Face Description with Local Binary Patterns: Application to Face Recognition
TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Journal ArticleDOI
From few to many: illumination cone models for face recognition under variable lighting and pose
TL;DR: A generative appearance-based method for recognizing human faces under variation in lighting and viewpoint that exploits the fact that the set of images of an object in fixed pose but under all possible illumination conditions, is a convex cone in the space of images.
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