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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yuting Hu Zhiling Long Ghassan AlRegib Multimedia & Sensors Lab (MSL) Center for Signal and Information Processing (CSIP) School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta 30332-0250 USA

ISBN: (纸本)9781509041183

In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multi-scale extended local binary patterns (ELBP) with rotation-invariant and uniform mappings to capture robust local micro- and macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale. Finally, we select the maximum values from the corresponding bins of multi-scale ELBP histograms at different scales as scale-invariant features. A comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows that the proposed SSELBP has high accuracy comparable to state-of-the-art texture descriptors on gray-scale-, rotation-, and scale-invariant texture classification but uses only one-third of the feature dimension.

关键词： Local binary pattern (LBP) extended LBP (ELBP) local descriptor scale invariant texture classification binary pattern multi-scale texture descriptor Scale formation histograms texture ultrasonic motion detector GRAIN pattern pattern recognition comprehensive assessment textures scales Scale

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A 3D Face Recognition Method Using Region-Based Extended Local binary pattern

A 3D Face Recognition Method Using Region-Based Extended Loc...

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IEEE International Conference on Image Processing

作者： Shiwen Lv Feipeng Da Xing Deng School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education

ISBN: (纸本)9781479983407

A 3D face recognition method using region-based extended local binary pattern (eLBP) is proposed. First, the depth image converted from the preprocessed 3D pointclouds is normalized. Then, different regions according to their distortions under facial expressions are extracted by binary masks and represented by the uniform pattern of extended LBP. Finally, sparse representation classifier (SRC) is adopted for classification on the single region. Feature-level and score-level fusion with weight-sparse representation classifier (W-SRC) are also tested and compared, and the latter has better performance. The experiments on FRGC v2.0 database demonstrate that the proposed method is robust and efficient.

关键词： 3D face recognition Extended local binary pattern Depth image binary mask Weight-sparse representation classifier Face recognition techniques binary pattern Facial Recognition Facial Expression Classifiers

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Dynamic Background Subtraction using Local binary pattern and Histogram of Oriented Gradients

Dynamic Background Subtraction using Local Binary Pattern an...

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International Conference on Image Information Processing

作者： Deepak Kumar Panda Sukadev Meher Dept. of Electronics and Communication Engg. National Institute of Technology Rourkela

ISBN: (纸本)9781509001491

Moving object detection in the presence of complex dynamic backgrounds such as swaying of trees, spouting of water from fountain, ripples in water, flag fluttering in the wind, camera jitters, noise, etc., is known to be very difficult and challenging task. In addition to this, illumination variation, camouflage and real-time constraint aggravate the problem further. Background subtraction (BS) is a widely used algorithm for moving object detection in the presence of static cameras. Its performance purely depends on the choice of features used for background modeling. In this paper, we have proposed a novel multi-feature and multi-modal based background subtraction using Local binary pattern (LBP) and Histogram of oriented Gradients (HOG) for complex dynamic scene. Each pixel is modeled as a set of multi-feature calculated from its neighborhood and multi-modal BS is performed using Gaussian mixture model (GMM). To show its efficacy, the proposed algorithm is compared with some of the state-of-the-art BS techniques. In order to evaluate the algorithm in uncontrolled environments, a collection of publicly available database has been used. Quantitative and qualitative results justify our algorithm for efficient moving object detection in the presence of swaying of trees, camouflage and ripples in the water surface.

关键词： Visual surveillance Motion detection Back-ground subtraction Non-stationary scene Camouflage Illumination invariant mimicry (behavior) Camouflage moving target detection Visual surveillance Motion detection histograms binary pattern wobbling Subtraction ripples Time constraints Bahamas

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Group Method Data Handling Neural Network for CT Abdomen Image Segmentation based on First Order Statistics and Local binary pattern

Group Method Data Handling Neural Network for CT Abdomen Ima...

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International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)

作者： S. N Kumar Ajay Kumar H A. Lenin Fred V. Suresh W. Abisha G. Brenda ECE Mar Ephraem College of Engineering and Technolgy Marthandam India CSE Mar Ephraem College of Engineering and Technolgy Marthandam India

ISBN: (纸本)9781538694831

This work proposes Group method Data Handling (GMDH) neural network for the segmentation of liver and liver tumor on abdomen CT images. The structure of the GMDH neural network is automatically structured using heuristic self-organization. Prior to segmentation, Nonlinear Tensor Diffusion (NLTD) filter was used for the preprocessing of input images. Feature extraction was performed by first order statistics and local binary pattern. The parameters of neural network like the number of useful input variables, the number of neurons in each layer and the selection of optimum neural network architecture are determined by using the error criterion derived from AIC (Akaike's Information Criterion). The performance of the GMDH algorithm was evaluated by success and error rates, similarity measures and the results outperform the back propagation neural network algorithm. The algorithms are developed in Matlab 2013a and tested on real time abdomen CT datasets. The satisfactory results were obtained by GMDH algorithm and are useful for computer aided diagnosis of liver cancer.

关键词： cancer computerised tomography data handling feature extraction image filtering image segmentation liver medical image processing neural nets tumours Neural network image filtering image segmentation data processing X-Ray Computed Tomography Medical image processing ct of abdomen binary pattern Feature extraction Tumours Liver error criterion

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Face Recognition with Enhanced Local Gabor binary pattern from Human Fixations

Face Recognition with Enhanced Local Gabor Binary Pattern fr...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Eunsoo Choi Seong-Whan Lee Christian Wallraven Department of Computer Science and Engineering Korea University Department of Brain and Cognitive Engineering Korea University

ISBN: (纸本)9781467317139

Performance of automatic face recognition algorithm has increased considerably over the past decades. However, face recognition under changes in lighting conditions remains a challenging issue for computers. In this paper, we propose a novel face recognition algorithm inspired by information taken from human fixation patterns. We augment a LGBP (Local Gabor binary pattern) algorithm - a well-known face recognition algorithm - to allocate different weights to each facial part during processing. For deriving the weights, we analyzed data from a human face recognition experiment using eye-tracking. Eye-tracking allows us to determine the facial parts during the recognition process which represent salient regions for human processing. Face images are pre-processed during the recognition step using a weight mask based on the salient regions from the eye-tracking data. A comparison with the standard non-weighted LGBP approach demonstrates the efficacy of our method with the weighted method performing better under lighting changes.

关键词： Face recognition Human perception LGBP Eye-tracking Lighting changes Facial Recognition face recognition algorithm binary pattern Face Face Recognition (Psychology) Computers Lighting

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Local binary pattern Special Investigation based on Search image face texture recognition

Local Binary Pattern Special Investigation based on Search i...

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International Symposium on Computer, Consumer and Control

作者： Hsien-Chih Hu Hsin-Ching Chou Yeong-Chin Chen Chang-Pei Yi Dept. of Comput. Sci. & Inf. Eng. Nat. Changhua Univ. of Educ. Changhua Taiwan

ISBN: (纸本)9781509030729

The local binary pattern approach, a texture descriptor method proposed by Ojala et al., has gained increased acceptance due to its computational simplicity and more importantly for encoding a powerful signature for describing textures. LBP feature extraction of edge face detection, in the light of with human face discrimination influence different angles test recognition efficiency. The algorithm presents some limitations such as lack of rotational invariance which have led to many proposals or extensions in order to overcome such limitations. In this paper we performed a modified algorithm of the original LBP proposal together with other recently proposed LBP extensions. Experimental results demonstrated the effectiveness and robustness of the described texture descriptors for images that are subjected to geometric or radiometric changes.

关键词： Image edge detection Search methods Laplace equations Character recognition Face recognition Face Edge detection Search methods Laplace's equation Character recognition Facial Recognition Face Face binary pattern texture descriptor LBP gene LBP

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HUMAN DETECTION USING DISCRIMINATIVE AND ROBUST LOCAL binary pattern

HUMAN DETECTION USING DISCRIMINATIVE AND ROBUST LOCAL BINARY...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Amit Satpathy Xudong Jiang How-Lung Eng Nanyang Technological University School Of EEE Nanyang Avenue Singapore 639798 Institute for Infocomm Research Agency for Science Technology and Research 1 Fusionopolis Way Connexis Singapore 138632

ISBN: (纸本)9781479903573

Despite superior performance of Local binary pattern (LBP) in texture classification and face detection, its performance in human detection has been limited for two reasons. Firstly, LBP differentiates a bright human from a dark background and vice-versa. This increases the intra-class variation of humans. Secondly, LBP is contrast and illumination invariant. It does not discriminate between weak contrast local regions and similar strong contrast ones, resulting in a similar feature representation. Non-Redundant LBP (NRLBP) has been proposed to solve the first issue of LBP. However, an inherent limitation of NRLBP is that LBP codes and their complements in the same block are mapped to the same code. Furthermore, NRLBP, like LBP, is also contrast and illumination invariant. In this paper, we propose a novel edge-texture feature, Discriminative Robust Local binary pattern (DRLBP), for human detection. DRLBP alleviates the problems of LBP and NRLBP by considering the weighted sum and absolute difference of a LBP code and its complement. Our experimental results show that DRLBP consistently outperforms LBP and NRLBP for human detection.

关键词： local binary pattern feature extraction human detection LBP pedestrian detection binary pattern LBP gene LBP Feature extraction Complement System Proteins binary Codes absolute difference Facial Recognition Human

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Empirical Survival Error Potential Weighted Least Squares for binary pattern Classification

Empirical Survival Error Potential Weighted Least Squares fo...

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International Conference on Control, Automation, Robotics & Vision

作者： Lei Sun Kar-Ann Toh Zhiping Lin Badong Chen School of Information and Electronics Beijing Institute of Technology Beijing PR China 100081 School of Electrical and Electronic Engineering Yonsei University Seoul Korea 120-749 School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an PR China 710049

ISBN: (纸本)9781479952007

A weighted least squares scheme based on an empirical survival error potential function is proposed in this paper. The empirical survival error potential function provides an error compensation scheme for noise distributions far from being Gaussian. This error compensation procedure is efficiently implemented via a weighted least squares formulation where an analytical solution form is obtained. The performance of the developed scheme is extensively tested on 16 benchmark data sets where the results show promising potential of the proposed empirical survival error distribution compensation scheme for binary pattern classification.

关键词： Least squares method pattern recognition Error compensation binary pattern potential function Dataset Scheme

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Angular pattern and binary Angular pattern for Shape Retrieval

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2014年第3期23卷 1118-27页

作者： Hu, Rong-Xiang Jia, Wei Ling, Haibin Zhao, Yang Gui, Jie Chinese Acad Sci Inst Nucl Energy Safety Technol Hefei 230031 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Peking Univ Sch Elect & Comp Engn Shenzhen 518055 Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China

In this paper, we propose two novel shape descriptors, angular pattern (AP) and binary angular pattern (BAP), and a multiscale integration of them for shape retrieval. Both AP and BAP are intrinsically invariant to scale and rotation. More importantly, being global shape descriptors, the proposed shape descriptors are computationally very efficient, while possessing similar discriminability as state-of-the-art local descriptors. As a result, the proposed approach is attractive for real world shape retrieval applications. The experiments on the widely used MPEG-7 and TARI-1000 data sets demonstrate the effectiveness of the proposed method in comparison with existing methods.

关键词： Shape retrieval angular features binary pattern multi-scale representation

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Efficient manipulation of binary data using pattern matching

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JOURNAL OF FUNCTIONAL PROGRAMMING 2006年第1期16卷 35-74页

作者： Gustafsson, P Sagonas, K Uppsala Univ Dept Informat Technol S-75105 Uppsala Sweden

pattern matching is an important operation in functional programs. So far, pattern matching has been investigated in the context of structured terms. This article presents an approach to extend pattern matching to terms without (much of a) structure such as binaries which is the kind of data format that network applications typically manipulate. After introducing the binary datatype and a notation for matching binary data against patterns, we present an algorithm that constructs a decision tree automaton from a set of binary patterns. We then show how the pattern matching using this tree automaton can be made adaptive, how redundant tests can be avoided, and how we can further reduce the size of the resulting automaton by taking interferences between patterns into account. Since the size of the tree automaton is exponential in the worst case, we also present an alternative new approach to compiling binary pattern matching which is conservative in space and analyze its complexity properties. The effectiveness of our techniques is evaluated using standard packet filter benchmarks and on implementations of network protocols taken from actual telecom applications.

关键词： pattern matching Functional programming network applications binary data Automata Network protocols binary binary pattern Robot

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