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2012 International Conference on Image Processing, Computer Vision, and pattern Recognition, IPCV 2012

作者： Mandava, Ajay K. Regentova, Emma E. Egiazarian, Karen Department of Electrical and Computer Engineering University of Nevada Las Vegas NV 89154 United States Institute of Signal Processing Tampere University of Technology Finland

ISBN: (纸本)9781601322258

A novel local binary pattern (LBP) based adaptive diffusion is presented for image denoising. The LBP operator unifies traditionally divergent statistical and structural models of region analysis. We use LBP textons to classify an image around a pixel into noisy, homogenous, corner and edge regions. According to different types of regions, a variable weight is introduced into the diffusion equation, so that the algorithm can adoptively encourage strong diffusion in homogenous/noisy regions and less on the edge/corner ones. Quantitative analyses based on peak signal to noise ratio shows the high performance of the method.

关键词： local binary pattern

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HIDDEn: Robust Data Hiding for Medical Images with Encryption and local binary pattern

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2024年第7期43卷 4439-4459页

作者： Priyanka Baranwal, Naman Singh, K. N. Singh, Om Prakash Singh, A. K. NIT Dept CSE Patna India IIT Dept CSE Kanpur India JIIT Dept CSE & IT Noida India IIIT Dept CSE Bhagalpur India

The secure transmission of medical images and related digital patient records on untrusted channels has recently become a focus in healthcare industries. Data hiding and encryption are important tools for this goal. This paper proposes a data-hiding method for medical images in the context of invisibility, robustness, security and low time cost. A dual watermarking is introduced to accomplish chaos-based encryption to ensure medical images' copyright protection and content security. First, a local binary pattern based on neighbouring pixels is used to compute an optimal value, called an embedding factor, for embedding both marks. Second, the host medical image is marked using the lifting wavelet transform, the lower-upper (LU) decomposition and singular value decomposition with an embedding factor to protect ownership. Last, the marked image is encrypted by using a 3D-chaotic map. The method is tested on two standard datasets, which is convenient for medical applications. Our experimental results and performance analysis demonstrate that the proposed scheme produces a peak signal-to-noise ratio (PSNR) and NCwat1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\text{NC}}}_{{\text{wat}}1}$$\end{document}/NCwat2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\text{NC}}}_{{\text{wat}}2}$$\end{document} of 54.82 dB and 0.9916/0.9928, respectively. Furthermore, the key space analysis of our encryption technique is greater than 2100\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setle

关键词： Medical images LWT local binary pattern LU decomposition Chaotic map RSVD

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Early detection of Alzheimer's disease using local binary pattern and convolutional neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第19期80卷 29585-29600页

作者： Francis, Ambily Pandian, Immanuel Alex Karunya Inst Technol & Sci Dept Elect & Commun Coimbatore Tamil Nadu India Sahrdaya Coll Engn & Technol Dept Elect & Commun Kodakara Kerala India

Alzheimer's disease, a progressive and irreversible abnormality of the human brain impairs memory and thinking skills. Gradually, it will damage the ability to carry out simple tasks. Even though the disease cannot be completely cured by medical specialists, the rate of brain damage can be pared if the disease is identified in its budding stage itself. Thus, victims and their relatives will get ample time to prepare themselves. Alzheimer's disease (AD), cognitively normal (CN), mild cognitive impairment convertible (MCIc), and mild cognitive impairment non-convertible (MCInc) are the different phases of cognition. The state of memory loss in aged people, which will not lead to AD, can be encountered as MCInc. The state-MCIc gradually leads to AD. The work is intended for the early detection of AD. Early detection can be claimed if and only if the state-MCIc is detected. But the clinical visual identification of state-MCIc from MRI scan is difficult. In this work, a novel local feature descriptor is proposed for the detection of state-MCIc. The proposed local feature descriptor combined strengths of fast Hessian detector and local binary pattern texture operator for the identification of key points and descriptions. A simple convolutional neural network is used for classification. The classification accuracy between MCIc and CN is obtained as 88.46% which is a pivotal result for early detection of AD. The classification accuracy between AD and CN is attained at 88.99%. The results indicate that the proposed system can contribute a colossal innovation in the early detection of AD.

关键词： Alzheimer's disease Mild cognitive impairment convertible Mild cognitive impairment non-convertible Fast Hessian matrix local binary pattern Convolutional neural network

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Maximal multi-channel local binary pattern with colour information for CBIR

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第35-36期79卷 25357-25377页

作者： Vimina, E. R. Divya, M. O. Amrita Sch Arts & Sci Dept Comp Sci & IT Amrita Viswa Vidyapeetham Kochi Campus Ernakulam India

Content Based Image Retrieval (CBIR) focuses on retrieving images from repositories based on visual features extracted from the images. Texture and colour are one of the popularly used feature combination in CBIR. A major challenge in colour image retrieval is the characterization of features of the constituent channels and their integration. The commonly adopted methodology include extraction of features of various channels followed by their concatenation. However, the resulting image feature vector is generally of high dimensionality. To address this problem, in this paper a texture-colour descriptor is proposed integrating the multi-channel features. For texture computation, a fixed sized local intensity based descriptor, Maximal Multi-channel local binary pattern (MMLBP), which integrates the multi-channel local binary information through an adder-map followed by thresholding is introduced. The histogram of the obtained patterns is used for representing the image texture. Colour information is captured by quantizing the RGB colour space and is represented with histogram. The colour-texture descriptors are further fused to characterize the images. The efficacy of the descriptor is evaluated by carrying out retrieval on benchmarked datasets for image retrieval such as Wang's 1 K, Corel 5 K, Corel 10 K, Coloured Brodatz Texture and Zubud, using precision and recall measures as evaluation metrics. It is observed that the proposed descriptor presents improved retrieval performance over the databases under consideration and outperforms other descriptors.

关键词： CBIR local binary pattern Multi-channel feature extraction RGB colour quantization Feature fusion Image matching

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Pyramid and multi kernel based local binary pattern for texture recognition

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第3期11卷 1241-1252页

作者： Tuncer, Turker Dogan, Sengul Firat Univ Technol Fac Dept Digital Forens Engn Elazig Turkey

In this article, a novel pyramid and multi kernel based method is proposed to increased success of the local binary pattern (LBP). Signum, ternary and quaternary binary feature extraction functions are used together and these are utilized as mathematical kernel of the LBP. In order to extract features in depth, pyramid model is used. Texture images are resized in the 4 levels to create pyramid. Finally, 5120 features are extracted from each level. In the feature reduction phase, principle component analysis is considered and linear discriminant analysis is utilized as classifier. To obtain numerical results, UIUC, Outex and USPTex datasets were used. The proposed method was compared to the other state of art texture classification methods. The recognition rates were calculated as 96.10%, 89.90% and 97.30% for UIUC, Outex and USPTex respectively. The robustness tests were performed using the Gaussian and salt and pepper noises. The best accuracy rates of the noisy images were calculated as 79.5% and 94.3% respectively. The experimental results proved the success of the proposed method.

关键词： Multi kernel LBP Pyramid structure local binary pattern Texture analysis Texture classification

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A compact local binary pattern using maximization of mutual information for face analysis

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pattern RECOGNITION 2011年第3期44卷 532-543页

作者： Jun, Bongjin Kim, Taewan Kim, Daijin Pohang Univ Sci & Technol Dept Comp Sci & Engn Pohang 790784 South Korea

Although many variants of local binary patterns (LBP) are widely used for face analysis due to their satisfactory classification performance, they have not yet been proven compact. We propose an effective code selection method that obtain a compact LBP (CLBP) using the maximization of mutual information (MMI) between features and class labels. The derived CLBP is effective because it provides better classification performance with smaller number of codes. We demonstrate the effectiveness of the proposed CLBP by several experiments of face recognition and facial expression recognition. Our experimental results show that the CLBP outperforms other LBP variants such as LBP. ULBP, and MCT in terms of smaller number of codes and better recognition performance. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： local binary pattern Feature selection Compact LBP Maximization of mutual information Face recognition Facial expression recognition

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Recognition of gas-liquid two-phase flow patterns based on improved local binary pattern operator

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INTERNATIONAL JOURNAL OF MULTIPHASE FLOW 2010年第10期36卷 793-797页

作者： Zhang, Wenyin Shih, Frank Y. Jin, Ningde Liu, Yinfeng Linyi Normal Univ Sch Informat Linyi 276005 Shandong Peoples R China New Jersey Inst Technol Coll Comp Sci Newark NJ 07102 USA Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China

A new method to pattern recognition of gas-liquid two-phase flow regimes based on improved local binary pattern (LBP) operator is proposed in this paper. Five statistic features are computed using the texture pattern matrix obtained from the improved LBP. The support vector machine and back-propagation neural network are trained to flow pattern recognition of five typical gas-liquid flow regimes. Experimental results demonstrate that the proposed method has achieved better recognition accuracy rates than others. It can provide reliable reference for other indirect measurement used to analyze flow patterns by its physical objectivity. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： local binary pattern Two-phase flow regime Support vector machine Neural network

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Face recognition using color local binary pattern from mutually independent color channels

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2013年第1期2013卷 1-11页

作者： Anbarjafari, Gholamreza Cyprus Int Univ Dept Elect & Elect Engn KKTC TR-10 Lefkosa Mersin Turkey

In this article, a high performance face recognition system based on local binary pattern (LBP) using the probability distribution functions (PDFs) of pixels in different mutually independent color channels which are robust to frontal homogenous illumination and planer rotation is proposed. The illumination of faces is enhanced by using the state-of-the-art technique which is using discrete wavelet transform and singular value decomposition. After equalization, face images are segmented by using local successive mean quantization transform followed by skin color-based face detection system. Kullback-Leibler distance between the concatenated PDFs of a given face obtained by LBP and the concatenated PDFs of each face in the database is used as a metric in the recognition process. Various decision fusion techniques have been used in order to improve the recognition rate. The proposed system has been tested on the FERET, HP, and Bosphorus face databases. The proposed system is compared with conventional and the state-of-the-art techniques. The recognition rates obtained using FVF approach for FERET database is 99.78% compared with 79.60 and 68.80% for conventional gray-scale LBP and principle component analysis-based face recognition techniques, respectively.

关键词： Illumination robust local binary pattern Face recognition Probability distribution function Discrete wavelet transform Kullback-Leibler distance

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Multi-resolution approach to human activity recognition in video sequence based on combination of complex wavelet transform, local binary pattern and Zernike moment

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第24期81卷 34863-34892页

作者： Khare, Manish Jeon, Moongu Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar Gujarat India Gwangju Inst Sci & Technol GIST Sch Elect Engn & Comp Sci Gwangju South Korea

Human activity recognition is a challenging problem of computer vision and it has different emerging applications. The task of recognizing human activities from video sequence exhibits more challenges because of its highly variable nature and requirement of real time processing of data. This paper proposes a combination of features in a multiresolution framework for human activity recognition. We exploit multiresolution analysis through Daubechies complex wavelet transform (DCxWT). We combine local binary pattern (LBP) with Zernike moment (ZM) at multiple resolutions of Daubechies complex wavelet decomposition. First, LBP coefficients of DCxWT coefficients of image frames are computed to extract texture features of image, then ZM of these LBP coefficients are computed to extract the shape feature from texture feature for construction of final feature vector. The Multi-class support vector machine classifier is used for classifying the recognized human activities. The proposed method has been tested on various standard publicly available datasets. The experimental results demonstrate that the proposed method works well for multiview human activities as well as performs better than some of the other state-of-the-art methods in terms of different quantitative performance measures.

关键词： Daubechies complex wavelet transform Human activity recognition local binary pattern Multiresolution analysis Video surveillance Zernike moment

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A modified local binary pattern based on homogeneity criterion for robust edge detection

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第5期17卷 2315-2322页

作者： Aboutabit, Noureddine Sultan Moulay Slimane Univ IPIM Lab ENSA Khouribga POB 523 Beni Mellal 23000 Morocco

Today, the local binary pattern (LBP) has become one of the most widely used texture descriptors thanks to its invariance and efficiency. The basic LBP method encodes local features by considering the difference in the local neighbourhood to represent a given image using the binary pattern histogram. Without performing the histogram step, the LBP method could be used to detect edges in an image. In this paper, two algorithms for edge detection are proposed. They are based on modifying the principle of the LBP method where a local neighbourhood is coded in binary by integrating a criterion of its homogeneity. In this work, we define this criterion as the ratio of the total variation in the whole image to the local variation of the neighbourhood. Thus, a new approach of edge detection is presented in two versions according to the way of calculating the differences in a neighbourhood. Experimental results on a standard natural image database show that the two proposed algorithms significantly improve the MSE, PSNR and SSIM indicators of the famous Canny detector and the improved LBP approach. In noisy conditions, our proposed algorithms present a better robustness to three types of noise.

关键词： Edge detection local binary pattern Homogeneity criteria Image processing

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