检索结果-内蒙古大学图书馆

Breast mass classification on mammograms using radial local ternary patterns

COMPUTERS IN BIOLOGY AND MEDICINE 2016年 72卷 43-53页

作者： Muramatsu, Chisako Hara, Takeshi Endo, Tokiko Fujita, Hiroshi Gifu Univ Grad Sch Med Dept Intelligent Image Informat 1-1 Yanagido Gifu 5011194 Japan Nagoya Med Ctr Dept Adv Diag 4-1-1 Nakaku Sannomaru Nagoya Aichi 4600001 Japan Higashi Nagoya Natl Hosp Dept Breast Surg Meito Ku 5-101 Umemorizaka Nagoya Aichi 4658620 Japan

Textural features can be useful in differentiating between benign and malignant breast lesions on mammograms. Unlike previous computerized schemes, which relied largely on shape and margin features based on manual contours of masses, textural features can be determined from regions of interest (ROIs) without precise lesion segmentation. In this study, therefore, we investigated an ROI-based feature, namely, radial local ternary patterns (RLTP), which takes into account the direction of edge patterns with respect to the center of masses for classification of ROIs for benign and malignant masses. Using an artificial neural network (ANN), support vector machine (SVM) and random forest (RF) classifiers, the classification abilities of RLTP were compared with those of the regular local ternary patterns (LW), rotation invariant uniform (RIU2) LTP, texture features based on the gray level co-occurrence matrix (GLCM), and wavelet features. The performance was evaluated with 376 ROIs including 181 malignant and 195 benign masses. The highest areas under the receiver operating characteristic curves among three classifiers were 0.90, 0.77, 0.78, 0.86, and 0.83 for RLTP, LW, RIU2-LTP, GLCM, and wavelet features, respectively. The results indicate the usefulness of the proposed texture features for distinguishing between benign and malignant lesions and the superiority of the radial patterns compared with the conventional rotation invariant patterns. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： local binary patterns local ternary patterns Texture feature Breast masses Mammograms Classification

来源：评论

学校读者我要写书评

暂无评论

Color treatment in endoscopic image classification using multi-scale local color vector patterns

引用

MEDICAL IMAGE ANALYSIS 2012年第1期16卷 75-86页

作者： Haefner, M. Liedlgruber, M. Uhl, A. Vecsei, A. Wrba, F. Salzburg Univ Dept Comp Sci A-5020 Salzburg Austria St Elizabeth Hosp Dept Internal Med Vienna Austria St Anna Childrens Hosp Endoscopy Unit A-1090 Vienna Austria Med Univ Vienna Dept Clin Pathol Vienna Austria

In this work we propose a novel method to describe local texture properties within color images with the aim of automated classification of endoscopic images. In contrast to comparable local binary patterns operator approaches, where the respective texture operator is almost always applied to each color channel separately, we construct a color vector field from an image. Based on this field the proposed operator computes the similarity between neighboring pixels. The resulting image descriptor is a compact 1D-histogram which we use for a classification using the k-nearest neighbors classifier. To show the usability of this operator we use it to classify magnification-endoscopic images according to the pit pattern classification scheme. Apart from that, we also show that compared to previously proposed operators we are not only able to get competitive classification results in our application scenario, but that the proposed operator is also able to outperform the other methods either in terms of speed, feature compactness, or both. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Classification Colon cancer local binary patterns Multi-scale Color

来源：评论

学校读者我要写书评

暂无评论

Texture image Classification based on improved local Quinary patterns

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第14期78卷 18995-19018页

作者： Armi, Laleh Fekri-Ershad, Shervan Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran

Texture image classification is an active research topic in computer vision that play an important role in many applications such as visual inspection systems, object tracking, medical image analysis, image segmentation, etc. So far, there are many descriptors for texture image analysis such as local binary patterns (LBP). LBP is a nonparametric operator, which describes the local spatial structure and the local contrast of an image. local quinary patterns (LQP) is one of the improved versions of LBP in terms of classification accuracy. Statistic input parameters and don't providing significant binary patterns are some disadvantages of LQP. In this paper a new version of LBP is proposed, which is known as improved local quinary patterns (ILQP). In this paper, a new definition is proposed to divide local quinary codes to four binary patterns. Each extracted binary patterns represent a subset of local features. Also, a new algorithm is proposed here to provide dynamic thresholds in dividing process of LQP. The proposed approach is evaluated using Outex, and Brodatz data sets. Our approach has been compared with some state-of-the-art methods. It is experimentally demonstrated that the proposed approach achieves the highest accuracy in comparison with most of the state-of-the-art texture classification approaches. Low computational complexity, rotation invariant, low impulse-noise sensitivity and high usability are advantages of the proposed texture analysis descriptor.

关键词： Texture image classification local Quinary patterns local binary patterns Feature extraction

来源：评论

学校读者我要写书评

暂无评论

An investigation on the use of local multi-resolution patterns for image classification

引用

INFORMATION SCIENCES 2016年 361卷 1-13页

作者： Gonzalez, Elena Bianconi, Francesco Fernandez, Antonio Univ Vigo Sch Ind Engn Campus Univ Vigo 36310 Spain Univ Perugia Dept Engn Via G Duranti 93 I-06125 Perugia Italy

The subject of this study is the use of local multi-dimensional patterns for image classification. The contribution is both theoretical and experimental: on the one hand the paper introduces a complete and general mathematical model for encoding multi-resolution, rotation-invariant local patterns;on the other experimentally evaluates the use of multi resolution patterns for image classification both from an information- and performance based standpoint. The results indicate that the joint multi-resolution model proposed in the paper can actually convey an additional amount of information with respect to the marginal model;but also that the marginal model (i.e. concatenation of features computed at different resolutions) can be a good enough approximation for practical applications. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Rotation-invariant multi-resolution patterns local binary patterns Texture spectrum Image classification

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Facial Expression Recognition Model Using local Feature Fusion of Gabor Wavelets and local Directionality patterns

引用

INTERNATIONAL JOURNAL OF AMBIENT COMPUTING AND INTELLIGENCE 2020年第1期11卷 48-70页

作者： Bellamkonda, Sivaiah Gopalan, N. P. Natl Inst Technol Dept Comp Applicat Tiruchirappalli Tamil Nadu India

Facial expression analysis and recognition has gained popularity in the last few years for its challenging nature and broad area of applications like HCI, pain detection, operator fatigue detection, surveillance, etc. The key of real-time FER system is exploiting its variety of features extracted from the source image. In this article, three different features viz. local binary pattern, Gabor, and local directionality pattern were exploited to perform feature fusion and two classification algorithms viz. support vector machines and artificial neural networks were used to validate the proposed model on benchmark datasets. The classification accuracy has been improved in the proposed feature fusion of Gabor and LDP features with SVM classifier, recorded an average accuracy of 93.83% on JAFFE, 95.83% on CK and 96.50% on MMI. The recognition rates were compared with the existing studies in the literature and found that the proposed feature fusion model has improved the performance.

关键词： Artificial Neural Networks Facial Emotion Recognition Feature Fusion Gabor Wavelets local binary patterns local Directionality patterns Machine Vision Support Vector Machines

来源：评论

学校读者我要写书评

暂无评论

local Ternary patterns from Three Orthogonal Planes for human action classification

引用

EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 5125-5128页

作者： Nanni, Loris Brahnam, Sheryl Lumini, Alessandra Univ Bologna DEIS I-40136 Bologna Italy Missouri State Univ Springfield MO 65804 USA

Human action classification is a new field of study with applications ranging from automatically labeling video segments to recognition of suspicious behavior in video surveillance cameras. In this paper we present some variants of local binary patterns from Three Orthogonal Planes (LBP-TOP), which is considered one of the state of the art texture descriptors for human action classification. The standard LBP-TOP operator is defined as a gray-scale invariant texture measure, derived from the standard local binary patterns (LBP). It is obtained by calculating the LBP features from the xt and yt planes of a space-time volume. Our LBP-TOP variants combine the idea of LBP-TOP with local Ternary patterns (LTP). The encoding of LTP is used for the evaluation of the local gray-scale difference in the different planes of the space-time volume. Different histograms are concatenated to form the feature vector and a random subspace of linear support vector machines is used for classifying action using the Weizmann database of video images. To the best of our knowledge, our method offers the first set of classification experiments to obtain 100% accuracy using the 10-class Weizmann dataset. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Human action classification local binary patterns local Ternary patterns Machine learning techniques Ensemble of support vector machines

来源：评论

学校读者我要写书评

暂无评论

Weighted Finite Automata based on local patterns for Image Authentication 9

Weighted Finite Automata based on Local Patterns for Image A...

引用

IEEE 9th International Conference on Semantic Computing

作者： Raju, U. S. N. George, Shibin Sandeep, Irlanki Kiran, Kothuri Sai Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal 506004 Telangana India

ISBN: (纸本)9781479979356

This paper presents an efficient image authentication system. The authentication signature is extracted from WFA encoding of the image. For noises that are more textural rather than color-based, we transform the image using a local-binary-Pattern filter, which is then converted to automata. We present a technique that incorporates the weights of the WFA, unlike previous works.

关键词： Weighted Finite Automata local binary patterns Signature Authentication

来源：评论

学校读者我要写书评

暂无评论

Age Estimation with local Ternary Directional patterns 1

引用

8th Pacific-Rim Symposium on Image and Video Technology (PSIVT)

作者： Angulu, Raphael Tapamo, Jules R. Adewumi, Aderemi O. Univ KwaZulu Natal Sch Math Stat & Comp Sci ZA-4000 Durban South Africa Univ KwaZulu Natal Sch Engn ZA-4041 Durban South Africa

ISBN: (数字)9783319757865

ISBN: (纸本)9783319757865;9783319757858

local texture descriptors have gained significant momentum in pattern recognition community due to their robustness compared to holistic descriptors. local ternary patterns and its variants use a static threshold to derive textural code in order to improve local binary patterns robustness to noise. It is not easy to select an optimum threshold in local ternary patterns and its variants for all images in a dataset or all experimental datasets. local directional patterns uses directional responses to encode image gradient. Apart from considering only k significant responses, local directional patterns does not include central pixel in determining image gradient. Disregarding central pixel and 8-k responses could result in lose of significant discriminative information. In this paper, we propose local ternary directional patterns that combines local ternary patterns and local directional patterns in determining image gradient. In local ternary directional patterns, the threshold is determined by the neighboring pixels and both significant, less significant responses and central pixel are considered in calculating image gradient. Evaluation of local ternary directional patterns on FG-NET dataset shows its robustness in local texture description compared to local directional pattern and local ternary pattern.

关键词： Age estimation local binary patterns local directional patterns local ternary directional patterns

来源：评论

学校读者我要写书评

暂无评论

Robust local Ternary patterns for Texture Categorization

Robust Local Ternary Patterns for Texture Categorization

引用

6th International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Han, Xian-Hua Xu, Gang Chen, Yen-Wei Ritsumeikan Univ Coll Informat Sci & Engn Shiga Japan

ISBN: (纸本)9781479927616

This paper proposes a new image representation for texture categorization, which is based on extension of local binary patterns (LBP). As we know LBP can achieve effective description ability with appearance invariance and adaptability of patch matching based methods. However, LBP only thresholds the differential values between neighborhood pixels and the focused one to 0 or 1, which is very sensitive to noise existing in the processed image. This study extends LBP to local ternary patterns (LTP), which considers the differential values between neighborhood pixels and the focused one as negative or positive stimulus if the absolute differential value is large;otherwise no stimulus (set as 0). With the ternary values of all neighbored pixels, we can achieve a pattern index for each local patch, and then extract the pattern histogram for image representation. Experiments on two texture datasets: Brodats32 and KTH TIPS2-a validate that the robust LTP can achieve much better performances than the conventional LBP and the state-of-the-art methods.

关键词： Texture recognition local ternary patterns local binary patterns micro-structures

来源：评论

学校读者我要写书评

暂无评论

local texton XOR patterns: A new feature descriptor for content-based image retrieval

引用

ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2016年第1期19卷 101-112页

作者： Bala, Anu Kaur, Tajinder St Baba Bhag Singh Inst Engn & Technol Dept Comp Sci Engn Jalandhar Punjab India St Baba Bhag Singh Inst Engn & Technol Dept Informat Technol Jalandhar Punjab India

In this paper, a novel feature descriptor, local texton XOR patterns (LTxXORP) is proposed for content-based image retrieval. The proposed method collects the texton XOR pattern which gives the structure of the query image or database image. First, the RGB (red, green, blue) color image is converted into HSV (hue, saturation and value) color space. Second, the V color space is divided into overlapping subblocks of size 2 x 2 and textons are collected based on the shape of the textons. Then, exclusive OR (XOR) operation is performed on the texton image between the center pixel and its surrounding neighbors. Finally, the feature vector is constructed based on the LTxXORPs and HSV histograms. The performance of the proposed method is evaluated by testing on benchmark database, Corel-1K, Corel-5K and Corel-10K in terms of precision, recall, average retrieval precision (ARP) and average retrieval rate (ARR). The results after investigation show a significant improvement as compared to the state-of-the-art features for image retrieval. (C) 2015, Karabuk University. Production and hosting by Elsevier B.V.

关键词： Texton local XOR patterns local binary patterns Histogram Texture Image retrieval

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：