检索结果-内蒙古大学图书馆

European Modelling Symposium

作者： Amer G. Binsaadoon El-Sayed M. El-Alfy Information and Computer Science Department College of Computer Sciences and Engineering King Fahd University of Petroleum and Minerals

ISBN: (纸本)9781509049721

Gait recognition has received increasing attention in biometrics. However, more effort is needed to enhance the performance. In this paper, we investigate a novel descriptor for gait recognition known as Kernel-based Fuzzy local binary Pattern (KFLBP). The spatio-temporal static and dynamic characteristics of a gait sequence is first summarized using a Gait-Energy Image (GEI). Then, the proposed approach combines multiple FLBP with different radii to better handle uncertainty in GEI and improve the recognition performance. We evaluate the proposed method on CASIA B dataset at different view angles. We also compare the performance with other feature extraction methods and explore the impact of different walking covariates on the performance.

关键词： Gait recognition Human motion Image processing Multi-kernel Fuzzy logic local binary patterns Gait energy image

来源：评论

学校读者我要写书评

暂无评论

An Automatic Physical Access Control System Based on Hand Vein Biometric Identification

引用

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2015年第3期61卷 320-327页

作者： Wang, Yiding Xie, Wei Yu, Xiaojie Shark, Lik-Kwan North China Univ Technol Coll Informat Engn Beijing Peoples R China Univ Cent Lancashire Sch Comp Engn & Phys Sci Preston PR1 2HE Lancs England

In consideration of an increasing demand to adopt the biometric technology to minimize potential security risks, the paper presents a consumer-level biometric system for automatic physical access control. Through development of a near infrared imaging device for biometric acquisition of dorsal hand vein patterns and computation efficient image processing methods, the proposed system is shown to possess a range of desirable operation characteristics for real-world use, which include reliable in identification, convenient to use, high in security, fast in response, low in cost, and simple for installation.

关键词： Access control Biometric identification Hand vein local binary patterns

来源：评论

学校读者我要写书评

暂无评论

Automated pollen identification using microscopic imaging and texture analysis

引用

MICRON 2015年 68卷 36-46页

作者： Victor Marcos, J. Nava, Rodrigo Cristobal, Gabriel Redondo, Rafael Escalante-Ramirez, Boris Bueno, Gloria Deniz, Oscar Gonzalez-Porto, Amelia Pardo, Cristina Chung, Francois Rodriguez, Tomas CSIC Inst Opt Spanish Natl Res Council Madrid Spain Univ Nacl Autonoma Mexico Mexico City 04510 DF Mexico Univ Castilla La Mancha VISILAB Dept E-13071 Ciudad Real Spain Ctr Agricola Marchamalo Guadalajara Spain Univ Complutense Fac Farm E-28040 Madrid Spain Tecnol Avanzadas Inspiralia Madrid Spain

Pollen identification is required in different scenarios such as prevention of allergic reactions, climate analysis or apiculture. However, it is a time-consuming task since experts are required to recognize each pollen grain through the microscope. In this study, we performed an exhaustive assessment on the utility of texture analysis for automated characterisation of pollen samples. A database composed of 1800 brightfield microscopy images of pollen grains from 15 different taxa was used for this purpose. A pattern recognition-based methodology was adopted to perform pollen classification. Four different methods were evaluated for texture feature extraction from the pollen image: Haralick's gray-level co-occurrence matrices (GLCM), log-Gabor filters (LGF), local binary patterns (LBP) and discrete Tchebichef moments (DTM). Fisher's discriminant analysis and k-nearest neighbour were subsequently applied to perform dimensionality reduction and multivariate classification, respectively. Our results reveal that LGF and DTM, which are based on the spectral properties of the image, outperformed GLCM and LBP in the proposed classification problem. Furthermore, we found that the combination of all the texture features resulted in the highest performance, yielding an accuracy of 95%. Therefore, thorough texture characterisation could be considered in further implementations of automatic pollen recognition systems based on image processing techniques. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Texture analysis Pollen identification Gray-level co-occurrence matrix Log-Gabor filters local binary patterns Discrete Tchebichef moments

来源：评论

学校读者我要写书评

暂无评论

Computer vision for virus image classification

引用

BIOSYSTEMS ENGINEERING 2015年 138卷 11-22页

作者： dos Santos, Fiorentino Luciano Caetano Paci, Michelangelo Nanni, Loris Brahnam, Sheryl Hyttinen, Jari Tampere Univ Technol Dept Elect & Commun Engn BioMediTech Tampere 33720 Finland Univ Padua Dept Informat Engn Padua Italy Missouri State Univ Comp Informat Syst Springfield MO 65804 USA

In this paper we present a new ensemble of descriptors for the classification of transmission electron microscopy images of viruses that is based on texture analysis. A set of six well-assessed texture descriptors, namely local binary Pattern (LBP), local Ternary Pattern (LTP), Dense LBP (DLBP), Rotation Invariant co-occurrence among LBP (RI), local Phase Quantization (LPQ), and LBP Histogram Fourier (LHF), are combined with innovative approaches to improve their performances in virus classification. To generate new variants of the aforementioned descriptors, different approaches are applied: (i) the Edge approach (ED), which extracts the textural information from specific regions of the image instead of from the original image;(ii) the Bag of Features (BOF) scheme, which is used to build vocabularies of the most representative patterns;and (iii) Multi-Quinary coding (MO). Moreover, to demonstrate the generalizability and applicability of ED and BOF, they are tested on additional datasets containing subcellular parts and tissues. We observe that ED improves the performance of the single standard descriptors, while BOF is particularly effective in fusion by sum rule with the standard application of texture descriptors (i.e., when they are extracted from the entire image). In contrast, the combination of MQ with LHF, DLBP, and RI does not improve virus classification. Using these results, we suggest a new ensemble of descriptors called NewF, which is based on the best methods investigated in this paper, as well as on some other state-of-the-art descriptors. NewF accuracy in virus classification is 85.7%, outperforming previous methods proposed in the literature for the same task (i.e., virus classification using the object scale dataset). The MATLAB code for our methods and NewF descriptor are available at https://***/node/2357. (C) 2015 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Texture descriptors Feature selection local binary patterns Ensemble of descriptors

来源：评论

学校读者我要写书评

暂无评论

Multi-Layer Sparse Representation for Weighted LBP-Patches Based Facial Expression Recognition

引用

SENSORS 2015年第3期15卷 6719-6739页

作者： Jia, Qi Gao, Xinkai Guo, He Luo, Zhongxuan Wang, Yi Dalian Univ Technol Sch Software Dalian 116621 Peoples R China

In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

关键词： facial expression recognition local binary patterns weighted patches sparse representation multi-layer model

来源：评论

学校读者我要写书评

暂无评论

Special Issue on Unsupervised Feature Learning for Vision

引用

IET COMPUTER VISION 2015年第5期9卷 627-628页

作者： Zhang, Shengping Zhang, Baochang Ye, Qixiang School of Computer Science and Technology Harbin Institute of Technology Weihai China Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Advanced Computer Studies (UMIACS) University of Maryland College Park United States

来源：评论

学校读者我要写书评

暂无评论

Face recognition using a novel image representation scheme and multi-scale local features

引用

INTERNATIONAL JOURNAL OF BIOMETRICS 2015年第3期7卷 191-212页

作者： Tao, Qing-Chuan Liu, Zhi-Ming Bebis, George Hussain, Muhammad Sichuan Univ Sch Elect & Informat Engn Chengdu Sichuan Peoples R China Univ Nevada Dept Elect & Biomed Engn Reno NV 89557 USA Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA King Saud Univ Dept Comp Sci Riyadh Saudi Arabia

This paper presents a new method for improving face recognition performance under difficult conditions. Specifically, a new image representation scheme is proposed which is derived from the YCrQ colour space using principal component analysis (PCA) followed by Fisher linear discriminant analysis (FLDA). A multi-scale local feature, LBP-DWT, is used for face representation which is computed by extracting different resolution local binary patterns (LBP) features from the new image representation and transforming the LBP features into the wavelet domain using discrete wavelet transform (DWT) and Haar wavelets. A variant of non-parametric discriminant analysis (NDA), called regularised non-parametric discriminant analysis (RNDA) is introduced to extract the most discriminating features from LBP-DWT. The proposed methodology has been evaluated using two challenging face databases (FERET and multi-PIE). The promising experimental results show that the proposed method outperforms two state-of-the-art methods, one based on Gabor features and the other based on sparse representation classification (SRC).

关键词： face recognition colour image local binary patterns LBP discrete wavelet transform DWT Fisher linear discriminant analysis FLDA non-parametric discriminant analysis NDA

来源：评论

学校读者我要写书评

暂无评论

EEG artifact elimination by extraction of ICA-component features using image processing algorithms

引用

JOURNAL OF NEUROSCIENCE METHODS 2015年第0期243卷 84-93页

作者： Raduentz, T. Scouten, J. Hochmuth, O. Meffert, B. Fed Inst Occupat Safety & Hlth Mental Hlth & Cognit Capac D-10317 Berlin Germany Humboldt Univ Dept Comp Sci D-12489 Berlin Germany

Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Independent component analysis ICA EEG Artifact elimination Image processing local binary patterns Range filter Geometric features

来源：评论

学校读者我要写书评

暂无评论

An automated detection of glaucoma using histogram features

引用

International Journal of Ophthalmology(English edition) 2015年第1期8卷 194-200页

作者： Karthikeyan Sakthivel Rengarajan Narayanan Electronics and Communication Engineering K.S.R. College of Engineering Electrical and Electronics Engineering Bannari Amman Institute of Technology

Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve. Hence, early detection diagnosis and treatment of an eye help to prevent the loss of vision. In this paper, a novel method is proposed for the early detection of glaucoma using a combination of magnitude and phase features from the digital fundus images. local binary patterns(LBP) and Daugman’s algorithm are used to perform the feature set *** histogram features are computed for both the magnitude and phase components. The Euclidean distance between the feature vectors are analyzed to predict glaucoma. The performance of the proposed method is compared with the higher order spectra(HOS)features in terms of sensitivity, specificity, classification accuracy and execution time. The proposed system results 95.45% output for sensitivity, specificity and classification. Also, the execution time for the proposed method takes lesser time than the existing method which is based on HOS features. Hence, the proposed system is accurate, reliable and robust than the existing approach to predict the glaucoma features.

关键词： Daugman's algorithm Euclidean distance glaucoma higher order spectra histogram features local binary patterns

来源：评论

学校读者我要写书评

暂无评论

A Real-time Face Recognition System Based on IP Camera and Image Sets Algorithm 12

A Real-time Face Recognition System Based on IP Camera and I...

引用

12th IEEE Int Conf Ubiquitous Intelligence & Comp/12th IEEE Int Conf Autonom & Trusted Comp/15th IEEE Int Conf Scalable Comp & Commun & Associated Workshops/IEEE Int Conf Cloud & Big Data Comp/IEEE Int Conf Internet People

作者： Gan, JunYing Wang, XiaoLin Zhai, YiKui Wuyi Univ Sch Informat Engn Jiangmen Peoples R China

ISBN: (纸本)9781467372114

We design a real-time face recognition system based on IP camera and image set algorithm by way of OpenCV and C++ programming development. The system includes three parts: preview module, register module and recognition module. Firstly, we apply the AdaBoost algorithm into the real-time face detection and join the nose detection for secondary screening. And then we extract texture feature from the detected face images by the method of LPQ(local Phase Quantization) or LBP(local binary patterns). Finally face is recognized based on image sets. After testing, the system can process video real-timely and has a good effect on face detection and recognition.

关键词： Face Recognition System Image Sets Classification local binary patterns local Phase Quantization IP Camera

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：