检索结果-内蒙古大学图书馆

Using Fractal and local binary pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere

引用

INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2016年第6期26卷 1650022-1650022页

作者： Xu, Fangzhou Zhou, Weidong Zhen, Yilin Yuan, Qi Wu, Qi Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China Qilu Univ Technol Sch Elect Engn & Automat Jinan 250353 Peoples R China Troops 72465 PLA Jinan 250022 Peoples R China Shandong Normal Univ Coll Phys & Elect Jinan 250014 Peoples R China Shandong Univ Qilu Hosp Jinan 250012 Peoples R China

The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

关键词： local binary pattern brain-computer interface electrocorticogram motor imagery

来源：评论

学校读者我要写书评

暂无评论

A novel feature extraction algorithm for Haar local binary pattern texture based on human vision system

引用

International Journal of Simulation: Systems, Science and Technology 2016年第14期17卷 11.1-11.5页

作者： Tao, Liu Department of Electronic Engineering Shaanxi Energy Institute XianyangShaanxi China

The locality and edges of texture image may be ignored by the Haar local binary pattern texture features owing to strong subjectivity and poor ability to self-adaptive to the artificial setting judgment threshold. Therefore, from the perspective of Human Vision System (HVS), the new Haar local binary pattern texture feature extraction algorithm (HLBP_HVS) is proposed. The local and global structure information of images are obtained and the self-adaptive and local optimal judgment threshold are calculated by the analysis of HVS influence factors, which include: texture detail and distribution of spatial position. The new Haar local binary pattern texture feature HLBP_HVS, which is objective and conforms to the image texture details and distribution of spatial position, could be extracted. The experimental results show that the proposed algorithm can effectively avoid the influence of the artificial judgment threshold on the texture detail and reflects the structure information of the image. Through comparison and analysis of the test results, we suggest that the accuracy of classification for Brodatz texture library also can be further improved. © 2016, UK Simulation Society. All rights reserved.

关键词： local binary pattern

来源：评论

学校读者我要写书评

暂无评论

local binary pattern based Face Recognition System for Automotive Security

Local Binary Pattern based Face Recognition System for Autom...

引用

International Conference on Signal Processing, Computing and Control (ISPCC)

作者： Patil, Shailaja A. Deore, Pramod J. RC Patel Inst Technol Elect & Telecommun Engn Shirpur 425405 India

ISBN: (纸本)9781479984367

In this paper we present an approach for face recognition under varying poses, illumination variation and facial expressions. Illumination variation, Pose variation and facial expressions are the main challenges among the various factors that severely affects the performance of the face recognition. The main aim of this paper is to calculate and evaluate the performance of combination of Independent Component Analysis and local binary pattern approach for different face databases that contains number of images with illumination variation, varying poses and facial expressions. 3 images per subject are used for training purpose and remaining images for testing or recognition purpose. Uniform local binary pattern is used for extracting the features. These extracted features reduced by independent component analysis and Euclidean distance is used for matching. We have calculated FRR and TSR parameter which gives accuracy of given method. Finally comparing the results for different face databases.

关键词： Face recognition local binary pattern Independent Component Analysis Euclidean distance

来源：评论

学校读者我要写书评

暂无评论

Texture segmentation by a new variant of local binary pattern

引用

Advances in Intelligent Systems and Computing 2016年 380卷 385-392页

作者： Prakash, Mosiganti Joseph Kezia, J.M. CSE Department Stanley College of Engineering and Technology for Women Affiliated to Osmania University Abids Chapel Road Near L B Stadium Hyderabad Telangana500001 India ECE Department Stanley College of Engineering & Technology for women Affiliated to Osmania University Abids Chapel Road Near L B Stadium Hyderabad Telangana500001 India

ISBN: (纸本)9788132225225

This paper highlights the local binary pattern (LBP) method in the unsupervised texture segmentation task. It has been made into a really dominant measure of image texture, showing outstanding results in terms of computational complexity and accuracy. The LBP operator is a theoretically simple yet very efficient approach for texture analysis. The LBP concept is slightly modified, i.e., instead of considering the center pixel value for generation of binary values, the present paper utilized average of all the eight neighboring pixels of the center pixel. The binary code generated is separated into "Diamond-LBP Code (DLBPC)" and "Corner LBP code (CLBPC)." The proposed new variant local binary pattern (NVLBP) segmentation approach is simple, rotationally invariant and easy to understand. This method also resulted in good segmentation which is noticed from the entropy, standard deviation, contrast, and discrepancy values. © Springer India 2016.

关键词： local binary pattern

来源：评论

学校读者我要写书评

暂无评论

local binary pattern orientation based face recognition 40

Local binary pattern orientation based face recognition

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Shen, Yi-Kang Chiu, Ching-Te Department of Computer Science National Tsing Hua University Taiwan

ISBN: (纸本)9781467369978

Scale-invariant feature transform (SIFT) is a feature point based method using the orientation descriptor for pattern recognition. It is robust under the variation of scale and rotation changes, but the computation cost increases with its feature points. local binary pattern (LBP) is a pixel based texture extraction method that achieves high face recognition rate with low computation time. We propose a new descriptor that combines the LBP texture and SIFT orientation information to improve the recognition rate using limited number of interest points. By adding the LBP texture information, we could reduce the SIFT orientation number in the descriptor by half. Therefore, we could reduce the computation time while keeping the recognition rate. In addition, we propose a matching method to reserve the effective matching pairs and calculate the similarity between two images. By combining these two methods, we can extract different face details effectively and further reduce computational cost. We also propose an approach using the region of interest (ROI) to remove the useless interest points for saving our computation time and maintaining the recognition rate. Experimental results demonstrate that our proposed LBP orientation descriptor can reduce around 30% computation time compared with the original SIFT descriptor while maintaining the recognition rate in FERET database. Adding the ROI at our proposed LBP orientation descriptor can reduce around 58% computation time compared with the original SIFT descriptor in FERET database. For extended YaleB database, our method has 1.2% higher recognition rate than original SIFT method and reduces 28.6% computational time. The experimental results with adding ROI reduces 61.9% computation time for YaleB database. © 2015 IEEE.

关键词： face recognition local binary pattern scale-invariant feature transform

来源：评论

学校读者我要写书评

暂无评论

Multiresolution local binary pattern variants based texture feature extraction techniques for efficient classification of microscopic images of hardwood species

引用

APPLIED SOFT COMPUTING 2015年 32卷 101-112页

作者： Yadav, Arvind R. Anand, R. S. Dewal, M. L. Gupta, Sangeeta Indian Inst Technol Dept Elect Engn Roorkee Uttar Pradesh India Forest Res Inst Dehradun Bot Div Dehra Dun Uttarakhand India

In this paper, multiresolution local binary pattern (MRLBP) variants based texture feature extraction techniques have been proposed to categorize hardwood species into its various classes. Initially, discrete wavelet transform (DWT) has been used to decompose each image up to 7 levels using Daubechies wavelet (db2) as decomposition filter. Subsequently, six texture feature extraction techniques (local binary pattern and its variants) are employed to obtain substantial features of these images at different levels. Three classifiers, namely, linear discriminant analysis (LDA), linear and radial basis function (RBF) kernel support vector machine (SVM), have been used to classify the images of hardwood species. Thereafter, classification results obtained from conventional and MRLBP variants based texture feature extraction techniques with different classifiers have been compared. For 10-fold cross validation approach, texture features acquired using discrete wavelet transform based uniform completed local binary pattern( DWTCLBPu2) feature extraction technique has produced best classification accuracy of 97.40 +/- 1.06% with linear SVM classifier. This classification accuracy has been achieved at the 3rd level of image decomposition using full feature (1416) dataset. Further, reduction in dimension of texture features (325 features) by principal component analysis (PCA) has been done and the best classification accuracy of 97.87 +/- 0.82% for DWTCLBPu2 at the 3rd level of image decomposition has been obtained using LDA classifier. The DWTCLBPu2 texture features have also established superiority among the MRLBP techniques with reduced dimension features for randomly divided database into fix training and testing ratios. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Discrete wavelet transform local binary pattern Completed local binary pattern Linear SVM Linear discriminant analysis RBF kernel SVM

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Efficient data mining for local binary pattern in texture image analysis

引用

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

来源：评论

学校读者我要写书评

暂无评论

Detection of Alzheimer's Disease Using Advanced local binary pattern from Hippocampus and Whole Brain of MR Images

Detection of Alzheimer's Disease Using Advanced Local Binary...

引用

International Joint Conference on Neural Networks

作者： Devvi Sarwinda Alhadi Bustamam Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Indonesia

ISBN: (纸本)9781509006212

Alzheimer's disease as one type of dementia can cause problems to human memory, thinking and behavior. The brain damage can be detected using brain volume and whole brain form. The correlation between brain shrinkage and reduction of brain volume can affect to deformation texture. In this research, the enhancement texture approach was proposed, called advanced local binary pattern (ALBP) method. ALBP is introduced as a 2D and 3D feature extraction descriptor. In the ALBP, sign and magnitude value were introduced as an enhancement to the previous LBP method. Due to a great number of features are produced by ALBP, the principal component analysis (PCA) and factor analysis are used as feature selection method. Furthermore, SVM classifier is applied for multiclass classification including Alzheimer's, mild cognitive impairment, and normal condition of whole brain and hippocampus. The experimental results from two scenarios (ALBP sign magnitude (2D) and ALBP sign magnitude using three orthogonal planes (3D) methods) show better accuracy and performance compare to previous method. Our proposed method achieved the average value of accuracy between 80% - 100% for both the whole brain and hippocampus data. In addition, uniform rotation invariant ALBP sign magnitude using three orthogonal planes as a 3D descriptor also outperforms other approaches with an average accuracy of 96.28% for multiclass classifications for whole brain image.

关键词： Texture analysis Alzheimer's disease Mild cognitive impairment local binary pattern Magnetic resonance image (MRI) Alzheimer Disease binary pattern Mild Cognitive Impairment brain morphology texture analysis Hippocampus Cerebrum

来源：评论

学校读者我要写书评

暂无评论

Ultrasound Image Despeckling based on local binary pattern and local Homogeneity

Ultrasound Image Despeckling based on Local Binary Pattern a...

引用

International Conference on Communication and Signal Processing

作者： Ajitha R. Subhamathi department of Computer Science University of Kerala

ISBN: (纸本)9781467385497

Speckle pattern is a form of multiplicative noise which blurs the ultrasound images. It reduces the contrast and resolution of ultrasound images which results in poor interpretation of image features. Hence speckle reduction is an important preprocessing step in many image processing tasks such as segmentation, classification and pattern recognition. Bilateral Filter has been proven to be very effective in denoising as it makes use of spatial averaging with out smoothing edges. The bilateral filter reappropriate the pixel intensity with a weighted sum of the pixels in its local neighborhood. The weights are computed based on intensity similarity and spatial proximity. The bilateral filter blurs the image structure when the intensity variations of the underlying images are very poor [1], [2], [3]. This work is an attempt to improve the structure preserving capability of bilateral filter by incorporating local structure of images such as local homogeneity and local binary pattern in computing weights.

关键词： Despeckling Bilateral Filtering local binary pattern local Homogeneity

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：