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Noise tolerant local binary pattern operator for efficient texture analysis

pattern RECOGNITION LETTERS 2012年第9期33卷 1093-1100页

作者： Fathi, Abdolhossein Naghsh-Nilchi, Ahmad Reza Univ Isfahan Dept Comp Engn Esfahan Iran

The local binary pattern (LBP) operator is a very effective multi-resolution texture descriptor that can be applied in many image processing applications. However, existing LBP operators can not use the information of non-uniform patterns efficiently and they are also sensitive to noise. This paper proposes a noise tolerant extension of LBP operators to extract statistical and structural image features for efficient texture analysis. The proposed LBP operator uses a circular majority voting filter and suitable rotation-invariant labeling scheme to obtain more regular uniform and non-uniform patterns that have better discrimination ability and more robustness against noise. Experimental results on the Brodatz, CUReT and MeasTex databases show the improvement of the proposed LBP operator performance, especially when a large number of neighbors are used for extracting texture patterns. (C) 2012 Elsevier B.V. All rights reserved.

关键词： local binary pattern Texture pattern descriptor Texture analysis

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Perceptual image hashing using transform domain noise resistant local binary pattern

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第7期80卷 9849-9875页

作者： Abbas, S. Qasim Ahmed, Fawad Chen, Yi-Ping Phoebe La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia HITEC Univ Dept Elect Engn Taxila Punjab Pakistan

A new Discrete Cosine Transform (DCT) domain Perceptual Image Hashing (PIH) scheme is proposed in this paper. PIH schemes are designed to extract a set of features from an image to form a compact representation that can be used for image integrity verification. A PIH scheme takes an image as the input, extracts its invariant features and constructs a fixed length output, which is called a hash value. The hash value generated by a PIH scheme is then used for image integrity verification. The basic requirement for any PIH scheme is its robustness to non-malicious distortions and discriminative ability to detect minute level of tampering. The feature extraction phase plays a major role in guaranteeing robustness and tamper detection ability of a PIH scheme. The proposed scheme fuses together the DCT and Noise Resistant local binary pattern (NRLBP) to compute image hash. In this scheme, an input image is divided into non-overlapping blocks. Then, DCT of each non-overlapping block is computed to form a DCT based transformed image block. Subsequently, NRLBP is applied to calculate NRLBP histogram. Histograms of all the blocks are concatenated together to get a hash vector for a single image. It is observed that low frequency DCT coefficients inherently have quite high robustness against non-malicious distortions, hence the NRLBP features extracted from the low frequency DCT coefficients provide high robustness. Computational results exhibit that the proposed hashing scheme outperforms some of the existing hashing schemes as well as can detect localized tamper detection as small as 3% of the original image size and at the same time resilient against non-malicious distortions.

关键词： Discrete cosine transform local binary pattern Perceptual image hashing Robust hash Transform domain hashing

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Contactless Palm Vein Recognition Using a Mutual Foreground-Based local binary pattern

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2014年第11期9卷 1974-1985页

作者： Kang, Wenxiong Wu, Qiuxia S China Univ Technol Sch Automat Sci & Engn Guangzhou 510631 Guangdong Peoples R China S China Univ Technol Guangzhou Inst Modern Ind Technol Guangzhou 510631 Guangdong Peoples R China

local binary pattern (LBP) is popular for the texture representation owing to its discrimination ability and computational efficiency, but when used to describe the sparse texture in palm vein images, the discrimination ability is diluted, leading to lower performance, especially for contactless palm vein matching. In this paper, an improved mutual foreground LBP method is presented for achieving a better matching performance for contactless palm vein recognition. First, the normalized gradient-based maximal principal curvature algorithm and k-means method are utilized for texture extraction, which can effectively suppress noise and improve accuracy and robustness. Then, an LBP matching strategy was adopted for similarity measurements on the basis of extracted palm veins and their neighborhoods, which include the vast majority of useful distinctive information for identification while eliminating interference by excluding the background. To further improve the LBP performance, the matched pixel ratio was adopted to determine the best matching region (BMR). Finally, the matching score obtained in the process of finding the BMR was fused with results of LBP matching at the score level to further improve the identification performance. A series of rigorous contrast experiments using the palm vein data set in the CASIA multispectral palmprint image database were conducted. The obtained low equal error rate (0.267%) and comparisons with the most state-of-the-art approaches demonstrate that our method is feasible and effective for contactless palm vein recognition.

关键词： Biometrics palm vein recognition local binary pattern mutual foreground principal curvatures matched pixel ratio

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Multi-scale region perpendicular local binary pattern: an effective feature for interest region description

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VISUAL COMPUTER 2015年第4期31卷 391-406页

作者： Thao-Ngoc Nguyen Miyata, Kazunori Japan Adv Inst Sci & Technol Sch Knowledge Sci Nomi Ishikawa 9231292 Japan

This paper proposes the perpendicular local binary pattern (PLBP) for efficiently describing textures in an interest region. Its novelty is two-fold: (1) the candidate generation scheme provides a set of patterns for each pixel, instead of conventionally assigning one pattern per pixel, and (2) an adaptive threshold based on the image contrast of a region is used. These modifications successfully enhance the robustness of PLBP to Gaussian noise as well as in near-uniform regions. We introduce the novel multi-scale region PLBP descriptor, which adopts the PLBP as its core feature. It defines multiple support regions from an interest point, sequentially performs ring-shaped and intensity order-based segmentations on each region, and pools PLBPs to corresponding segments. These steps are controlled easily by a set of parameters, thus offering high flexibility. Experimental results on challenging benchmarks, including three datasets of image matching and two datasets of object recognition, demonstrate the effectiveness of the proposed descriptor in handling common photometric and geometric transformations. It significantly improves the robustness, compared with current state-of-the-art descriptors, while maintaining a reasonable operational cost.

关键词： local binary pattern Perpendicular Intensity order Multi-support regions Interest regions Image matching Feature descriptor

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Efficient data mining for local binary pattern in texture image analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第9期42卷 4529-4539页

作者： Kwak, Jin Tae Xu, Sheng Wood, Bradford J. NIH Hlth Clin Ctr Ctr Intervent Oncol Bethesda MD 20892 USA

local binary pattern (LBP) is a simple gray scale descriptor to characterize the local distribution of the gray levels in an image. Multi-resolution LBP and/or combinations of the LBPs have shown to be effective in texture image analysis. However, it is unclear what resolutions or combinations to choose for texture analysis. Examining all the possible cases is impractical and intractable due to the exponential growth in a feature space. This limits the accuracy and time- and space-efficiency of LBP. Here, we propose a data mining approach for LBP, which efficiently explores a high-dimensional feature space and finds a relatively smaller number of discriminative features. The features can be any combinations of LBPs. These may not be achievable with conventional approaches. Hence, our approach not only fully utilizes the capability of LBP but also maintains the low computational complexity. We incorporated three different descriptors (LBP, local contrast measure, and local directional derivative measure) with three spatial resolutions and evaluated our approach using two comprehensive texture databases. The results demonstrated the effectiveness and robustness of our approach to different experimental designs and texture images. Published by Elsevier Ltd.

关键词： local binary pattern Frequent pattern mining Texture image Feature selection Classification

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An improved real-time object proposals generation method based on local binary pattern

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2017年第4期14卷

作者： Jiang, Yanting Yan, Jia Fan, Ci'en Shi, Wenxuan Deng, Dexiang Wuhan Univ Elect Informat Sch Wuhan 430072 Hubei Peoples R China

Generating a group of category-independent proposals of objects in an image within a very short time is an effective approach to accelerate traditional sliding window search, which has been widely used in preprocessing step of object recognition. In this article, we propose a novel object proposals generation method to produce an order set of candidate windows covering most of object instances. With combination of gradient and local binary pattern, our approach achieves better performance than BING in finding occluded objects and objects in dim lighting conditions. In experiments on the challenging PASCAL VOC 2007 data set, we show that our approach is significantly more accurate than BING. In particular, using 2000 proposals, we achieve 97.6% object detection rate and 69.3% mean average best overlap. Moreover, our proposed method is very efficient and takes only about 0.006 s per image on a laptop central processing unit. The detection speed and high accuracy of proposed method mean that it can be applied to recognizing specific objects in robot visions.

关键词： Objectness proposal generation local binary pattern computer vision object detection

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Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis

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

作者： Shahriar, Md Rifat Ahsan, Tanveer Chong, UiPil Univ Ulsan Dept Elect & Comp Engn Ulsan 680749 South Korea

Fault diagnosis of induction motors in the practical industrial fields is always a challenging task due to the difficulty that lies in exact identification of fault signatures at various motor operating conditions in the presence of background noise produced by other mechanical subsystems. Several signal processing approaches have been adopted so far to mitigate the effect of this background noise in the acquired sensor signal so that fault-related features can be extracted effectively. Addressing this issue, this paper proposes a new approach for fault diagnosis of induction motors utilizing two-dimensional texture analysis based on local binary patterns (LBPs). Firstly, time domain vibration signals acquired from the operating motor are converted into two-dimensional gray-scale images. Then, discriminating texture features are extracted from these images employing LBP operator. These local feature descriptors are later utilized by multi-class support vector machine to identify faults of induction motors. The efficient texture analysis capability as well as the gray-scale invariance property of the LBP operators enables the proposed system to achieve impressive diagnostic performance even in the presence of high background noise. Comparative analysis reveals that LBP8,1 is the most suitable texture analysis operator for the proposed system due to its perfect classification performance along with the lowest degree of computational complexity.

关键词： Fault diagnosis Induction motors local binary pattern Texture analysis Background noise Support vector machine

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Hexagonal Volume local binary pattern (H-VLBP) with deep stacked autoencoder for Human Action Recognition

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COGNITIVE SYSTEMS RESEARCH 2019年 58卷 71-93页

作者： Kiruba, K. Elizabeth, Shiloah D. Raj, Sunil Retmin C. Anna Univ Dept Comp Sci & Engn CEG Campus Chennai 600025 Tamil Nadu India Anna Univ Dept Informat Technol MIT Campus Chennai 600044 Tamil Nadu India

Human action recognition plays a significant role in a number of computer vision applications. This work is based on three processing stages. In the first stage, discriminative frames are selected as representative frames per action to minimize the computational cost and time. In the second stage, novel neighbourhood selection approaches based on geometric shapes including triangle, quadrilateral, pentagon, hexagon, octagon and heptagon are used in Volumetric local binary pattern (VLBP) to extract the features from frame sequences based on motion and appearance information. Hexagonal Volume local binary pattern (H-VLBP) descriptor has been found to produce better results among all other novel geometric shape based neighbourhood selection approaches for human action recognition. However, the dimensionality of extracted feature from H-VLBP is too large. Therefore, the deep stacked autoencoder is used for dimensionality reduction with the decoder layer replaced by softmax layer for performing multi-class recognition. The developed approach is applied to four publicly available benchmark datasets, namely KTH, Weizmann, UCF11 dataset and IXMAS dataset for human action recognition. The results obtained show that the proposed approach outperforms the state-of-art techniques. Moreover, the approach has been tested with a synthetic dataset and better results have been obtained. This illustrates the effectiveness of the approach in real time environment. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Action recognition local binary pattern H-VLBP Deep stacked autoencoder

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local binary pattern on hexagonal structure for face matching

Local binary pattern on hexagonal structure for face matchin...

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2007 International Conference on Image Processing, Computer Vision, and pattern Recognition, IPCV 2007

作者： He, Xiangjian Hintz, Tom Li, Jianmin Zhang, Huaifeng Wu, Qiang Jia, Wenjing Faculty of Information Technology University of Technology Sydney Australia School of Computer and Mathematics Fuzhou University China

ISBN: (纸本)9781601320438

Principal Components Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA), have been widely used for 2D face recognition. local binary pattern (LBP), however, provides a simpler and more effective way to represent faces. With LBP, face image is divided into small regions from which LBP histograms are extracted and concatenated into a single and global feature histogram representing the face image. The recognition is performed using Chi square and other commonly used dissimilarity measures. In this paper, we construct LBP codes together with three dissimilarity measures on hexagonal structure. We show that LBPs defined on hexagonal structure will lead to a faster and more accurate scheme for face recognition.

关键词： local binary pattern

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local binary pattern based Multimodal Biometric Recognition using Ear and FKP with Feature Level Fusion

Local Binary Pattern based Multimodal Biometric Recognition ...

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2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2019

作者： Kumar, A. Muthu Chandralekha, A. Himaja, Y. Sai, S. Mounika Kalsalingam Academy of Research and Education Dept. of Electronics and Communication Eng. Krishnankoil India

ISBN: (纸本)9781538695425

Biometrics is a mode of popularity and identification affirmation that uses physical attributes of a particular person that are impossible or as a minimum hard to mask. Finger-Knuckle-Print and ear print are examined to be maximum reliable physiological bio metric methods. These days a Finger Knuckle Print authentication based totally bio metric system has been efficaciously applied in various sectors. These unique Finger Knuckle Print and ear print authentication offers in reveals its user image that matches with its very own template that is stored inside the database for verification. An ear print is a replica of the parts outside the ear which have impressed a particular surface [3]. Many variants of local binary patterns are widely used for feature extraction process due to their satisfactory performance, for recognition purpose. An effective method of extracting feature patterns for Finger Knuckle Print (FKP) and ear has been proposed in this project. After extracting features of ear and FKP separately, they are fused by feature level fusion process which is done registration phase. The fused results of FKP and ear have been compared with the stored fused values in authentication phase. If the level of matching is achieved, that particular person gets authenticated to access the things else the person will be impostor [4]. This system gives more accuracy in less time. Challenges: The accuracy of authentication is improved in less duration. The chances of intruding is reduced. © 2019 IEEE.

关键词： local binary pattern

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