检索结果-内蒙古大学图书馆

Indirect immunofluorescence image classification using texture descriptors

EXPERT SYSTEMS WITH APPLICATIONS 2014年第5期41卷 2463-2471页

作者： Nanni, Loris Paci, Michelangelo Brahnam, Sheryl Univ Padua DEI Padua Italy Tampere Univ Technol ELT Tampere 33720 Finland Missouri State Univ Springfield MO 65804 USA BioMediTech Tampere 33520 Finland

In this work we propose an ensemble of texture descriptors for HEp-2 cell classification. Our system is based on a "pyramidal application" of local binary patterns coupled with a method for handling nonuniform bins. This feature extraction approach is then combined with a support vector machine (SVM) classifier. We test our method on a recent contest dataset (the MIVIA HEp-2 images dataset) using different testing protocols. This dataset is very challenging since the images are characterized by high variability in illumination. Therefore, to obtain good results, it is essential to apply a preprocessing algorithm: we choose the histogram equalization. We found that the best results are obtained when the original intensity images are converted into grayscale images with ten discrete values. Since a training set is provided in the contest dataset, we use it for descriptor selection and for parameter settings. The system built by using the training data is then applied to the testing set. Experiments show that our method outperforms the winner of the recent contest at the 21st International Conference on Pattern Recognition 2012. Our descriptors and MATLAB code will be available at webpage http://***/wdyn/?lDsezione=3314&2IDgruppo_pass=124&2preview=. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Texture descriptors local binary patterns Support vector machine Ensemble HEp-2 cells classification

来源：评论

学校读者我要写书评

暂无评论

Evaluation of texture features for automatic detecting butterfly species using extreme learning machine

引用

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2014年第2期26卷 267-281页

作者： Kaya, Yilmaz Kayci, Lokman Tekin, Ramazan Ertugrul, O. Faruk Siirt Univ Dept Comp Engn TR-56100 Siirt Turkey Siirt Univ Dept Biol TR-56100 Siirt Turkey Batman Univ Dept Comp Engn TR-72100 Batman Turkey Batman Univ Dept Elect & Elect Engn TR-72100 Batman Turkey

In this study, we present an application of extreme learning machine (ELM) and image processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. This paper evaluates the capability of butterfly species classification by using texture features of butterfly images. Two texture descriptors such as grey-level co-occurrence matrix (GLCM) and local binary patterns (LBP) were used for comparison purpose. ELM is employed for classification in butterfly-feature space. A total of 190 butterfly images belonging to 19 different species of Pieridae family were used. The identification accuracy of the proposed method was 98.25% and 96.45% with GLCM and LBP butterfly-feature spaces, respectively. The methodology presented herein effectively detected and classified these butterflies. These findings suggested that the proposed GLCM, LBP texture features extraction techniques and ELM algorithm are feasible and excellent in identification and classification of butterfly species.

关键词： butterfly identification expert system grey level co-occurrence matrix local binary patterns texture analysis extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

A graph construction method using LBP self-representativeness for outdoor object categorization

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年第0期36卷 294-302页

作者： Dornaika, F. Bosaghzadeh, A. Salmane, H. Ruichek, Y. Univ Basque Country UPV EHU San Sebastian Spain Basque Fdn Sci Ikerbasque Bilbao Spain UTBM IRTES SET F-90010 Belfort France

In this paper, we introduce a new graph construction algorithm that is useful for many semi-supervised learning tasks. Unlike the main stream for graph construction, our proposed data self-representativeness approach simultaneously estimates the graph structure and its edge weights through sample coding. Compared with the recent l(1) graph that is based on sparse coding, our proposed objective function has a closed-form solution and thus is more efficient than the iterative schemes deployed for solving the sparse coding problem. Our proposed method is inspired by the recent coding scheme "Weighted Regularized Least Square" (WRLS) proposed for improving the Sparse Representation Classifier. This paper has two main contributions. Firstly, we introduce a Two Phase Weighted Regularized Least Square (TPWRLS) graph construction that is based on self-representativeness of data samples. A key element of the proposed method is the second phase of coding that allows data closeness or locality to be naturally incorporated by solving a coding over some automatically selected relevant samples and by reinforcing the individual regularization terms according to the first phase coefficients. Secondly, the obtained data graph is used, in a semi-supervised context, in order to categorize detected objects in driving/urban scenes using local binary patterns as image descriptors. The experiments show that the proposed method can outperform competing methods. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Semi-supervised learning Graph-based label propagation local binary patterns Outdoor object classification

来源：评论

学校读者我要写书评

暂无评论

An improved local binary pattern for texture classification

引用

OPTIK 2014年第20期125卷 6320-6324页

作者： Dan, Zhiping Chen, Yanfei Yang, Zhi Wu, Guang China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hy Yichang 443002 Peoples R China Collaborat Innovat Ctr Geohazards & Ecoenvironm T Yichang 443002 Hubei Province Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Shennongjia Forest Zone Wenshui Forest Farm Shennongjia 442400 Hubei Peoples R China

This paper proposes an improved robust descriptor: the Joint local binary patterns with Weber-like responses ULBPW). The JLBPW descriptor mainly solves two problems of the existing local binary Pattern (LBP) method. The existing LBP methods discard the local intensity differences by holding their binary information. And the correlation of patterns under different scales is also ignored when the multi-resolution technique is used by the LBP methods. Aiming at the two problems, the JLBPW descriptor organizes the ignored local intensity differences according to the Weber's law that is discovered from psychological experiments. And the organized differences are set as responses of the corresponding joint patterns. The proposed descriptor supplies more information to describe local structures of images. Experiments on two representative databases demonstrate that the proposed descriptor is superior to other modern approaches. (C) 2014 Elsevier GmbH. All rights reserved.

关键词： Feature extraction Weber's law local binary patterns Multi-resolution

来源：评论

学校读者我要写书评

暂无评论

HEp-2 cells classification via sparse representation of textural features fused into dissimilarity space

引用

PATTERN RECOGNITION 2014年第7期47卷 2367-2378页

作者： Theodorakopoulos, Ilias Kastaniotis, Dimitris Economou, George Fotopoulos, Spiros Univ Patras Dept Phys Elect Lab Patras 26500 Greece

Autoimmune diseases are proven to be connected with the occurrence of autoantibodies in patient serum. Antinuclear autoantibodies (ANAs) identification can be accomplished in a laboratory using indirect immunofluorescence (IF) imaging. In this paper a system for automatic classification of staining patterns on HEp-2 fluorescence images is proposed. Our method utilizes two descriptors in order to encode gradient and textural characteristics of the depicted patterns. Along with distribution of SIFT features, we propose the new GoC-LBP descriptor based on co-occurrences of uniform local binary patterns along directions dictated by the orientation of local gradient. At a second stage, the descriptors are fused while creating a dissimilarity representation of an image. A powerful classification scheme is incorporated, utilizing a discriminative sparse representation-based scheme for the classification. Experiments were conducted using a publicly available dataset, comparing the obtained performance to recently reported results of a relevant contest, demonstrating the effectives of the proposed method. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： HEp-2 cells Staining patterns classification local binary patterns SIFT descriptors Dissimilarity fusion Dissimilarity representation Sparse representation Multiple-level representation

来源：评论

学校读者我要写书评

暂无评论

Facial expression recognition algorithm using LGC based on horizontal and diagonal prior principle

引用

OPTIK 2014年第16期125卷 4186-4189页

作者： Tong, Ying Chen, Rui Cheng, Yong Nanjing Inst Technol Dept Commun Engn Nanjing Jiangsu Peoples R China

The traditional local binary Pattern (LBP) algorithm can analyze the center pixel and neighboring pixels of the gray relationship, using in facial expression recognition, but you cannot consider the eyes, mouth, forehead and other areas in the expression feature different trends in the gradient direction. Firstly, we propose the local Gradient Coding (LGC) algorithm, though the binary encoding to the horizontal, vertical and diagonal gradients respectively, to produce the fusion characteristic, then this can fully describe the facial muscles texture, wrinkles and other local deformation of contains the expression information. On the other hand, in order to reduce the computational complexity, and to remove the redundant, while not lose the main information contained in the face texture expression. This paper proposes and optimizes a new LGC operator based on horizontal and diagonal gradient prior principle (LGC-HD). The experimental results from JAFFE database show that, LGC-HD algorithm is more quickly and effectively to extract facial expression feature than LGC algorithm. Comparing to the traditional LBP algorithm, LBP uniform pattern and Gabor filtering, this LGC-HD algorithm has a significant advantage in the recognition accuracy and run time. (C) 2014 Elsevier GmbH. All rights reserved.

关键词： local binary patterns local gradient coding Texture Facial expression recognition

来源：评论

学校读者我要写书评

暂无评论

引用

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2014年第6期38卷 508-516页

作者： Odstrcilik, Jan Kolar, Radim Tornow, Ralf-Peter Jan, Jiri Budai, Attila Mayer, Markus Vodakova, Martina Laemmer, Robert Lamos, Martin Kuna, Zdenek Gazarek, Jiri Kubena, Tomas Cernosek, Pavel Ronzhina, Marina St Anne Fac Hosp Brno ICRC Brno 65691 Czech Republic Brno Univ Technol Dept Biomed Engn Fac Elect Engn & Commun Brno 61600 Czech Republic Univ Erlangen Nurnberg Dept Ophthalmol D-91054 Erlangen Germany Univ Erlangen Nurnberg Pattern Recognit Lab D-91058 Erlangen Germany Univ Erlangen Nurnberg Erlangen Grad Sch Adv Opt Technol D-91058 Erlangen Germany Ophthalmol Clin MD Tomas Kubena Zlin 76000 Czech Republic

Images of ocular fundus are routinely utilized in ophthalmology. Since an examination using fundus camera is relatively fast and cheap procedure, it can be used as a proper diagnostic tool for screening of retinal diseases such as the glaucoma. One of the glaucoma symptoms is progressive atrophy of the retinal nerve fiber layer (RNFL) resulting in variations of the RNFL thickness. Here, we introduce a novel approach to capture these variations using computer-aided analysis of the RNFL textural appearance in standard and easily available color fundus images. The proposed method uses the features based on Gaussian Markov random fields and local binary patterns, together with various regression models for prediction of the RNFL thickness. The approach allows description of the changes in RNFL texture, directly reflecting variations in the RNFL thickness. Evaluation of the method is carried out on 16 normal ("healthy") and 8 glaucomatous eyes. We achieved significant correlation (normals: rho = 0.72 +/- 0.14;p << 0.05, glaucomatous: rho = 0.58 +/- 0.10;p << 0.05) between values of the model predicted output and the RNFL thickness measured by optical coherence tomography, which is currently regarded as a standard glaucoma assessment device. The evaluation thus revealed good applicability of the proposed approach to measure possible RNFL thinning. (c) 2014 The Authors. Published by Elsevier Ltd.

关键词： Glaucoma Retinal nerve fiber layer Texture analysis Fundus images local binary patterns Markov random fields

来源：评论

学校读者我要写书评

暂无评论

Facial expression recognition based on local binary patterns: A comprehensive study

引用

IMAGE AND VISION COMPUTING 2009年第6期27卷 803-816页

作者： Shan, Caifeng Gong, Shaogang McOwan, Peter W. Philips Res Labs NL-5656 AE Eindhoven Netherlands Univ London Dept Comp Sci London E1 4NS England

Automatic facial expression analysis is an interesting and challenging problem, and impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. In this paper, we empirically evaluate facial representation based on statistical local features, local binary patterns, for person-independent facial expression recognition. Different machine learning methods are systematically examined on several databases. Extensive experiments illustrate that LBP features are effective and efficient for facial expression recognition. We further formulate Boosted-LBP to extract the most discriminant LBP features, and the best recognition performance is obtained by using Support Vector Machine classifiers with Boosted-LBP features. Moreover, we investigate LBP features for low-resolution facial expression recognition, which is a critical problem but seldom addressed in the existing work. We observe in our experiments that LBP features perform stably and robustly over a useful range of low resolutions of face images, and yield promising performance in Compressed low-resolution video sequences captured in real-world environments. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Facial expression recognition local binary patterns Support vector machine Adaboost Linear discriminant analysis Linear programming

来源：评论

学校读者我要写书评

暂无评论

Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding

引用

INFORMATION 2014年第2期5卷 305-318页

作者： Chen, Ying Zhang, Shiqing Zhao, Xiaoming Taizhou Univ Inst Image Proc & Pattern Recognit Taizhou 317000 Peoples R China

Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS) sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP) and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE) database. Compared with other widely used methods such as linear support vector machines (SVM), sparse representation-based classifier (SRC), nearest subspace classifier (NSC), K-nearest neighbor (KNN) and radial basis function neural networks (RBFNN), the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks.

关键词： non-negative least-squares sparse coding local binary patterns facial expression recognition

来源：评论

学校读者我要写书评

暂无评论

FACE RECOGNITION WITH STATISTICAL local binary patterns

FACE RECOGNITION WITH STATISTICAL LOCAL BINARY PATTERNS

引用

International Conference on Machine Learning and Cybernetics

作者： Chen, Lei Wang, Yun-Hong Wang, Yi-Ding Huang, Di Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China North China Univ Technol Coll Informat Engn Beijing 100041 Peoples R China Ecole Cent Lyon MI Dept Lyon France

ISBN: (纸本)9781424447053

In this work, we present a novel algorithm for face recognition named statistical local binary patterns (sLBP). This is a further development of original local binary Pattern algorithm. Our method is applied for face recognition under visual light environment dealing with dramatically illumination varying on faces. After a statistical analysis on the distribution probability of the gray-level difference values between neighbor pixels, a mapping function is proposed to encode a wide range of these values into three binary bits. Three extension LBP layers are then generated. Finally the uniform pattern histograms of all these layers in every divided region are concatenated as an enhanced local feature vector of the face image. Experimental results on FERET face database show considerable effectiveness and robustness of our proposed method.

关键词： Face recognition local binary patterns uniform pattern statistical analysis

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：