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Objective evaluation of fabric pilling based on wavelet transform and the local binary pattern

TEXTILE RESEARCH JOURNAL 2012年第18期82卷 1880-1887页

作者： Jing, Junfeng Zhang, Zanzan Kang, Xuejuan Jia, Jianyuan Xian Polytech Univ Sch Elect & Informat Xian Peoples R China Xidian Univ Sch Elect & Mech Engn Xian Peoples R China Xian Aerotech Coll Dept Elect Xian Peoples R China

A new objective fabric pilling evaluation method based on wavelet transform and the local binary pattern (LBP) is developed. The surface pills are identified from the high-frequency noise, fabric texture, and illuminative variation of a pilled fabric image by the two-dimensional discrete wavelet transform. The energies of each detailed sub-image at scales 4-6 in three orientations (horizontal, vertical, and diagonal) and the LBP features of the reconstructed detail image from scales 3 to 6 are calculated as elements of the pilling feature vector to characterize the pilling intensity. These feature values are normalized and the vector dimensions are reduced by principal component analysis. Then the support vector machine, a kind of data mining tool, is used as a classifier to classify the pilling grades. The result suggests that the proposed method can successfully evaluate the pilling intensity of knitted fabrics and could be applicable to practical objective pilling evaluation.

关键词： Fabric pilling wavelet transform local binary pattern principal component analysis support vector machine

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Noise-Resistant local binary pattern With an Embedded Error-Correction Mechanism

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2013年第10期22卷 4049-4060页

作者： Ren, Jianfeng Jiang, Xudong Yuan, Junsong Nanyang Technol Univ Inst Media Innovat BeingThere Ctr Singapore 637553 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

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.

关键词： local binary pattern local ternary pattern uniform patterns noise resistance

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Real-Time Face Detection and Recognition via local binary pattern Plus Sample Selective Biomimetic pattern Recognition

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2013年第3期E96D卷 523-530页

作者： Zhai, Yikui Gan, Junying Li, Jinwen Zeng, Junying Xu, Ying Wuyi Univ Sch Informat Engn Jiangmen 529020 Guangdong Peoples R China Beijing Univ Aeronaut & Astronaut Sch Elect Informat & Engn Beijing 100191 Peoples R China S China Univ Technol Sch Automat Guangzhou 510641 Guangdong Peoples R China

Due to security demand of society development, real-time face recognition has been receiving more and more attention nowadays. In this paper, a real-time face recognition system via local binary pattern (LBP) plus Improved Biomimetic pattern Recognition (BPR) has been proposed. This system comprises three main steps: real-time color face detection process, feature extraction process and recognition process. Firstly, a color face detector is proposed to detect face with eye alignment and simultaneous performance;while in feature extraction step, LBP method is adopted to eliminate the negative effect of the light heterogeneity. Finally, an improved BPR method with Selective Sampling construction is applied to the recognition system. Experiments on our established database named WYU Database, PUT Database and AR Database show that this real-time. face recognition system can work with high efficiency and has achieved comparable performance with the state-of-the-art systems.

关键词： face detection face recognition local binary pattern biomimetic pattern recognition sample selection

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Completed robust local binary pattern for texture classification

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NEUROCOMPUTING 2013年 106卷 68-76页

作者： Zhao, Yang Jia, Wei Hu, Rong-Xiang Min, Hai Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China Chinese Acad Sci Inst Nucl Energy Safety Technol Hefei 230031 Peoples R China

Original local binary pattern (LBP) descriptor has two obvious demerits, i.e., it is sensitive to noise, and sometimes it tends to characterize different structural patterns with the same binary code which will reduce its discriminability inevitably. In order to overcome these two demerits, this paper proposes a robust framework of LBP, named Completed Robust local binary pattern (CRLBP), in which the value of each center pixel in a 3 x 3 local area is replaced by its average local gray level. Compared to the center gray value, average local gray level is more robust to noise and illumination variants. To make CRLBP more robust and stable, Weighted local Gray Level (WLG) is introduced to take place of the traditional gray value of the center pixel. The experimental results obtained from four representative texture databases show that the proposed method is robust to noise and can achieve impressive classification accuracy. (c) 2012 Elsevier B.V. All rights reserved.

关键词： local binary pattern Texture classification

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Evaluation of noise robustness for local binary pattern descriptors in texture classification

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2013年第1期2013卷 1页

作者： Kylberg, Gustaf Sintorn, Ida-Maria Uppsala Univ Ctr Image Anal Uppsala Sweden Swedish Univ Agr Sci Ctr Image Anal Uppsala Sweden

local binary pattern (LBP) operators have become commonly used texture descriptors in recent years. Several new LBP-based descriptors have been proposed, of which some aim at improving robustness to noise. To do this, the thresholding and encoding schemes used in the descriptors are modified. In this article, the robustness to noise for the eight following LBP-based descriptors are evaluated;improved LBP, median binary patterns (MBP), local ternary patterns (LTP), improved LTP (ILTP), local quinary patterns, robust LBP, and fuzzy LBP (FLBP). To put their performance into perspective they are compared to three well-known reference descriptors;the classic LBP, Gabor filter banks (GF), and standard descriptors derived from gray-level co-occurrence matrices. In addition, a roughly five times faster implementation of the FLBP descriptor is presented, and a new descriptor which we call shift LBP is introduced as an even faster approximation to the FLBP. The texture descriptors are compared and evaluated on six texture datasets;Brodatz, KTH-TIPS2b, Kylberg, Mondial Marmi, UIUC, and a Virus texture dataset. After optimizing all parameters for each dataset the descriptors are evaluated under increasing levels of additive Gaussian white noise. The discriminating power of the texture descriptors is assessed using tenfolded cross-validation of a nearest neighbor classifier. The results show that several of the descriptors perform well at low levels of noise while they all suffer, to different degrees, from higher levels of introduced noise. In our tests, ILTP and FLBP show an overall good performance on several datasets. The GF are often very noise robust compared to the LBP-family under moderate to high levels of noise but not necessarily the best descriptor under low levels of added noise. In our tests, MBP is neither a good texture descriptor nor stable to noise.

关键词： local binary pattern

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Evaluation of the wear label description in carpets by using local binary pattern techniques

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TEXTILE RESEARCH JOURNAL 2010年第20期80卷 2132-2143页

作者： Orjuela, Sergio Vansteenkiste, Ewout Rooms, Filip De Meulemeester, Simon Keyser, Robin Philips, Wilfried Univ Ghent Dept Telecommun & Informat Proc TELIN IPI IBBT B-9000 Ghent Belgium

Carpet manufacturers certify their products for end-use applications by evaluating the wear behavior of their carpets in mechanical experiments. Currently, this process is performed by visual inspection, suffering from subjective gathers that limit reliability. To automate this process, we propose the use of image processing techniques, specifically of local binary pattern (LBP) statistics. Such statistics are tolerant against illumination changes, can be easily implemented, and perform well when combined with a symmetrized adaptation of the Kullback-Leibler divergence. As a main innovation, we extend the existing rotationally invariant LBPs by including 'mirror' and 'complement' invariants. We show an accurately improved and more reliable estimation of the degree of wear in worn carpets. The evaluation is performed on four digital reference scales, each containing eight pairs of images comparing transitional degrees of wear to the original appearance. Additionally, the texture changes due to distortions of the pile yarn tufts are enhanced by choosing a suitable scale factor per reference. We validate the findings using six physical reference scales, each containing four pairs of images. In both references, linear correlations of over 0.89 are demonstrated between the degrees of wear and extracted features from the images. These findings justify the use of the proposed LBP extensions in a first approach towards an automated low-cost inspection system for carpet wear at low computation cost.

关键词： Carpet wear image processing local binary pattern texture inspection

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A novel approach for facial expression recognition using local binary pattern with adaptive window

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第2期80卷 2243-2262页

作者： Kola, Durga Ganga Rao Samayamantula, Srinivas Kumar JNTUK Dept ECE UCEK Kakinada 533003 India

Facial Expression Recognition (FER) is an important area in human computer interaction. FER has different applications such as analysis of student behaviour in virtual class room, driver mood detection, security systems, and medicine. The analysis of facial expressions is an interesting and exciting problem. Feature extraction plays important role in any FER system. local binary pattern (LBP) and its variants are popular for feature extraction due to simplicity in computation and monotonic illumination invariant property. However, the performance of LBP is poor in the presence of noise. This work proposes a novel approach for feature extraction to improve the performance of the FER. In this approach, the LBP is calculated considering 4-neighbors and diagonal neighbours separately. Further, for affective feature description, the concept of adaptive window and averaging in radial directions is introduced. This approach reduces the length of the feature vector as well as immune to noise. Support Vector Machine (SVM) is considered for classification. Recognition rate and confusion matrix are used to assess the performance of the proposed algorithm. Extensive experimental results on JAFFE, CK, FERG and FEI face databases show significant improvement in recognition rate compared to the available techniques both in noise free and noisy conditions.

关键词： Adaptive window Facial expression recognition Feature extraction local binary pattern Support vector machine

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The large-scale crowd analysis based on sparse spatial-temporal local binary pattern

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MULTIMEDIA TOOLS AND APPLICATIONS 2014年第1期73卷 41-60页

作者： Yang, Hua Cao, Yihua Su, Hang Fan, Yawen Zheng, Shibao Shanghai Jiao Tong Univ Dept EE Inst Image Commun & Informat Proc Shanghai 200240 Peoples R China Shanghai Key Lab Digital Media Proc & Transmiss Shanghai Peoples R China

As a particular class of public security issues, the large-scale crowd analysis plays a very important role in video surveillance application. This paper proposes a sparse spatial-temporal local binary pattern (SST-LBP) descriptor to extract dynamic texture of the walking crowd which can be applied to the crowd density estimation and distribution analysis. The proposed approach consists of four steps. First of all, sparse selected locations are extracted, which vary notably in both spatial domain and temporal domain. Afterwards, we propose a SST-LBP algorithm to extract the local dynamic feature and utilize the local feature's statistical property to describe the crowd feature. Thirdly, the overall crowd density level can be determined by classifying the crowd feature with support vector machine. Finally, the local feature is used to represent the local density and then the overall density distribution can be described. To improve the accuracy, we introduce the perspective correction into the detection of sparse selected locations and the spectrum analysis of SST-LBP code. The experiments on different datasets not only show that the proposed SST-LBP method is effective and robust on the large-scale crowd density estimation and distribution, but also indicate that the deformity correction is useful. Compared with other methods, the proposed method has the advantage of low computation complexity and high efficiency. In addition, it performs well on all density levels and can present local crowd distribution.

关键词： Video surveillance Crowd density local binary pattern Sparse point Density distribution

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Semi-fragile spatial watermarking based on local binary pattern operators

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OPTICS COMMUNICATIONS 2011年第16-17期284卷 3904-3912页

作者： Zhang Wenyin Shih, Frank Y. Linyi Univ Sch Informat Linyi 276005 Shandong Peoples R China New Jersey Inst Technol Coll Comp Sci Newark NJ 07102 USA

local binary pattern (LBP) operators, which measure the local contrast within a pixel's neighborhood, have been successfully applied to texture analysis, visual inspection, and image retrieval. In the paper, we present a novel semi-fragile spatial watermarking method based on LBP operators by using the local pixel contrast for the embedding and extraction of watermarks. We also propose a general framework for multi-level image watermarking. Experimental results show that the proposed watermarking methods are robust against commonly-used image processing operations, such as additive noise, luminance change, contrast adjustment, color balance, and JPEG compression. At the same time, they achieve good invisibility, fragility, and image tamper detection and localization with less computational cost. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Digital watermarking Multi-level watermark Semi-fragile watermark local binary pattern

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Single-stage object detector with local binary pattern for remote sensing images

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第13期44卷 4137-4162页

作者： Zhou, Kexue Zhang, Min Lin, Sheng Zhang, Rui Wang, Hai Xidian Univ Sch Aerosp Sci & Technol Xian Peoples R China Shaanxi Acad Aerosp Technol Applicat Co Ltd Remote Sensing Technol Dept Xian Peoples R China Xidian Univ Sch Aerosp Sci & Technol Xian 710126 Peoples R China

The objects in remote sensing images usually have complex background and appear anywhere in any direction, which poses challenges for deep learning-based object detection. In order to solve the above problems, we embed the local binary pattern (LBP) into deep learning-based object detection to facilitate performance improvements. Specifically, we implement thep LBP via depthwise separable convolution without any learnable parameters, which makes LBP highlight the object-related regions of feature map instead of original images. The convolutional implementation of LBP makes it easy to embed it in deep learning-based object detection, so that deep learning-based object detection can possess the ability of LBP to extract the texture information from complex background without having to learn. The gradients of the branch where LBP convolution is located are blocked and cannot be backpropagated according to proposed LBP convolution, so we propose an effective residual block, called LBPRes (Residual block with local binary pattern) for normal training. To deal with the problem of object direction changes, we introduce the rotation invariance into the LBPRes, denoted as RILBPRes (LBPRes with Rotation Invariance), which makes deep learning-based object detection have the local rotation invariance to cope with the object direction changes. Finally, we construct a single-stage object detection network S(2)LBPNet (Single Stage object detection Network with local binary pattern) with a local binary pattern and conduct related experiments on the DIOR and HRRSD datasets. According to the experimental results, with a small number of parameters added, S(2)LBPNet outperforms the YOLOv5s baseline by 2.2% and 5.1% on DIOR and HRRSD datasets, respectively, which prove the superiority of the proposed S(2)LBPNet.

关键词： local binary pattern object detection rotation invariance optical remote sensing

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