检索结果-内蒙古大学图书馆

25th International Conference on Pattern Recognition (ICPR)

作者： Himeur, Yassine Alsalemi, Abdullah Bensaali, Faycal Amira, Abbes Qatar Univ Dept Elect Engn Doha Qatar De Montfort Univ Inst Artificial Intelligence Leicester Leics England

ISBN: (纸本)9781728188089

Identifying domestic appliances in the smart grid leads to a better power usage management and further helps in detecting appliance-level abnormalities. An efficient identification can be achieved only if a robust feature extraction scheme is developed with a high ability to discriminate between different appliances on the smart grid. Accordingly, we propose in this paper a novel method to extract electrical power signatures after transforming the power signal to 2D space, which has more encoding possibilities. Following, an improved local binary patterns (LBP) is proposed that relies on improving the discriminative ability of conventional LBP using a post-processing stage. A binarized eigenvalue map (BEVM) is extracted from the 2D power matrix and then used to post-process the generated LBP representation. Next, two histograms are constructed, namely up and down histograms, and are then concatenated to form the global histogram. A comprehensive performance evaluation is performed on two different datasets, namely the GREEND and WITHED, in which power data were collected at 1 Hz and 44000 Hz sampling rates, respectively. The obtained results revealed the superiority of the proposed LBP-BEVM based system in terms of the identification performance versus other 2D descriptors and existing identification frameworks.

关键词： Appliance identification 2D power representation local binary patterns binarized eigenvalue map classification

来源：评论

学校读者我要写书评

暂无评论

Genetic-based feature fusion in face recognition using arithmetic coded local binary patterns

引用

IET IMAGE PROCESSING 2020年第15期14卷 3742-3750页

作者： Najafi Khanbebin, Saeed Mehrdad, Vahid Lorestan Univ Dept Elect & Elect Khorramabad Iran

local binary patterns (LBPs) are one of the attempts for gathering local features with face recognition algorithms. Although the application of LBP's in many recognition contents is too apparent, these methods have limited accuracy because of their threshold value. One problem is earning one value for two different regions with a diverse pixel neighbourhood, which causes mistakes in feature vector and decreases the discriminative power. In this study, the authors proposed a modified LBP that covers the LBP's disadvantages. The proposed approach is arithmetic coded LBP (ACLBP) that uses arithmetic coding process during LBP calculation instead of applying original thresholds. The proposed policy addresses the problem of returning one similar LBP value for two different patches. Moreover, the proposed method modifies LBP by using a different threshold for calculating the pixels differences. Using this algorithm, the authors conducted a genetic-based feature fusion method by combining LBP and histogram of oriented gradients and ACLBP. The proposed approach could work better on LFW dataset, and the ORL dataset and Yale face dataset that shows the improving role of ACLBP in comparison with the earlier version of LBP.

关键词： image colour analysis feature extraction face recognition image classification image texture image fusion arithmetic codes image coding genetic algorithms gradient methods ACLBP local binary patterns local features face recognition algorithms recognition contents threshold value diverse pixel neighbourhood feature vector modified LBP LBP's disadvantages arithmetic coding process LBP calculation original thresholds similar LBP value different patches different threshold pixels differences fusion method

来源：评论

学校读者我要写书评

暂无评论

Hyperdimensional Computing With local binary patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings

引用

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2020年第2期67卷 601-613页

作者： Burrello, Alessio Schindler, Kaspar Benini, Luca Rahimi, Abbas Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland Univ Bern Univ Hosp Bern Inselspital Sleep Wake Epilepsy CtrDept Neurol Bern Switzerland

Objective: We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG). Methods: Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes, from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them. Results: We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36-100 electrodes. For the majority of the patients (ten out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using k-fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% versus 94.77%) and macroaveraging accuracy (95.42% versus 94.96%), and 74x lower memory footprint, but slightly higher average latency in detection (15.9 s versus 14.7 s). Moreover, the algorithm can reliably identify (with a p-value < 0.01) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes. Conclusion and significance: Our algorithm provides: 1) a unified method for both learning and classification tasks with end-to-end binary operations;2) one-shot learning from seizure examples;3) linear computational scalability for increasing number of electrodes;and 4) generation of transparent codes that enables post-translational support for clinical decision m

关键词： iEEG one-shot learning local binary patterns symbolic dynamics hyperdimensional computing seizure detection localization of seizure onset zone

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Image Classification Based on Segmented local binary patterns

引用

SENSING AND IMAGING 2020年第1期21卷 1-16页

作者： Ye, Zhen Dong, Rui Bai, Lin Jin, Chengxuan Nian, Yongjian Changan Univ Sch Elect & Control Engn Xian Peoples R China Xian Inst Modern Control Technol Xian Peoples R China Third Mil Med Univ Army Med Univ Coll Biomed Engn & Imaging Med Chongqing 400038 Peoples R China

Recently, local binary patterns (LBP) coupled with principal component analysis has been developed for feature extraction of hyperspectral imagery, which has shown success over traditional methods but is limited in physical meaning representation due to the noise bands existing in hyperspectral data. In order to preserve the intrinsic geometrical structure of original data, we propose a segmented LBP (SLBP) to group correlative bands and then extract spatial-spectral features from each band group. The proposed approach employs the LBP operator on independent subspaces to characterize local texture information and distinct spectral signatures, along with a decision fusion system further improving discriminant power. The proposed approach is compared with several traditional and state-of-the-art methods on two benchmark datasets (i.e., the Indian Pines dataset and the Salinas Valley dataset). Experimental results demonstrate that the proposed SLBP strategy can yield superior classification performance (96.8% for the Indian Pines dataset with an improvement of approximately 6.4% and 4.2% when compared with LBP and MELBP, respectively;98.1% for the Salinas Valley dataset with an improvement of approximately 3.5% and 1.3% compared with LBP and MELBP, respectively).

关键词： Hyperspectral classification Principal component analysis local binary patterns Decision fusion

来源：评论

学校读者我要写书评

暂无评论

local binary patterns of Spatial Plane and Temporal Linear for Spotting Spontaneous Expression Frames 2019

Local Binary Patterns of Spatial Plane and Temporal Linear f...

引用

Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

作者： Xiaofeng Fu Jun Wu Xiaojuan Fu School of Computer Science and Technology Hangzhou DianZi University Hangzhou China Department of Basic Qinghai Communications Technical College Xining China

ISBN: (纸本)9781450376273

Spontaneous expression can reveal people's true emotions as comparing with traditional expression. Spotting spontaneous expression frames in the video is prerequisite for studying its characteristics. This paper proposes the local binary patterns feature extraction algorithm based on spatial plane and temporal line. Firstly, the improved normalized cross-correlation algorithm is used to finely match the eye region and the mouth region respectively. Then, fusing two kinds of features, one is the mean local binary patterns features of linear region which are extracted from the temporal linear, the other is the local binary patterns features of the sector region which are extracted from the spatial plane. Finally, the feature is converted into the frame feature by feature correlation function, and frame feature of the spontaneous expression frame is larger than threshold. The experimental results show that our algorithm performs well on the CAS(ME)2 database. The hitting rate of negative spontaneous expression segments increases by 27% than the ULBP algorithm. Simultaneously, the AUC value of the ROC curve increases by 1% as comparing to the LBP-TOP algorithm.

关键词： Spontaneous expression videos local binary patterns Spotting Spatial plane Temporal linear

来源：评论

学校读者我要写书评

暂无评论

Applying maximally stable extremal regions and local binary patterns for guide-wire detecting in percutaneous coronary intervention

引用

IET IMAGE PROCESSING 2019年第13期13卷 2579-2586页

作者： Pusit, Prasong Xie, Xiao-Liang Hou, Zeng-Guang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

Intervention surgery strongly requires information on the guide-wire position under the monitoring of X-ray video. Hence, the related researches such as guide-wire detecting or tracking have become widespread. However, most of the existing methods require a lot of resources for computing or large data for training since the X-ray videos have internal physicals such as anatomical skeleton contours and organs that are quite similar to a guide-wire. This work presents a practical method that only requires a moderate number of training data for detecting a guide-wire tip in an X-ray video sequence during the percutaneous coronary intervention surgery. The method applies maximally stable extremal regions (MSER) combine with modified multi-filters (region area range filter and stroke width variation filter) for region detection and local binary patterns (LBP) for guide-wire recognition. The motivation for applying MSER and LBP are the robust efficacy and the low requirement of resources. The approach evaluated 20 different sequences of X-ray videos, a total of 1295 frames. 50 selected frames were used as training templates and others to experiment. The method was successfully performed to the detecting guide-wires with p-value < 0.01 compared with conventional MSER methods, 93.7% average detection accuracy, and 21 fps average speed.

关键词： blood vessels medical image processing surgery image sequences video signal processing image filtering object detection X-ray imaging object tracking stroke width variation filter region detection local binary patterns guide-wire recognition conventional MSER methods maximally stable extremal regions guide-wire position anatomical skeleton contours training data X-ray video sequence percutaneous coronary intervention surgery region area range filter X-ray video monitoring guide-wire tip detection modified multifilters training templates

来源：评论

学校读者我要写书评

暂无评论

Automated gait-based gender identification using fuzzy local binary patterns with tuned parameters

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第7期10卷 2495-2504页

作者： El-Alfy, E-S M. Binsaadoon, A. G. King Fand Univ Petr & Minerals Coll Comp Sci & Engn Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia

With the rapid advances in sensor networks and internet-of-things, several applications have evolved to ubiquitously monitor what is happening in smart environments. This paper designs a gait-based gender identification system based on an improved method of fuzzy local binary patterns for texture analysis. The moving person's silhouette is extracted and a texture image is constructed to summarize structural and dynamical variations over one gait cycle. Then, histograms representing soft pattern variations are constructed and used as feature vectors. The proposed method employs grid search to find the optimal hyper-parameters for the feature extraction method. Finally, support vector machines are trained to predict the gender of the walking person. The performance is evaluated and compared with four other local binary pattern (LBP) related texture descriptors. The results show significant improvements in the performance under different walking conditions.

关键词： Surveillance video Gender recognition Gait energy image Support vector machine Texture feature extraction local binary patterns

来源：评论

学校读者我要写书评

暂无评论

Convolutional neural networks and local binary patterns for hyperspectral image classification

引用

EUROPEAN JOURNAL OF REMOTE SENSING 2019年第1期52卷 448-462页

作者： Wei, Xiangpo Yu, Xuchu Liu, Bing Zhi, Lu Informat Engn Univ Zhengzhou Henan Peoples R China

Convolutional neural networks (CNNs) have strong feature extraction capability, which have been used to extract features from the hyperspectral image. local binary pattern (LBP) is a simple but powerful descriptor for spatial features, which can lessen the workload of CNNs and improve the classification accuracy. In order to make full use of the feature extraction capability of CNNs and the discrimination of LBP features, a novel classification method combining dual-channel CNNs and LBP is proposed. Specifically, a one-dimensional CNN (1D-CNN) is adopted to process original hyperspectral data to extract hierarchical spectral features and another same 1D-CNN is applied to process LBP features to further extract spatial features. Then, the concatenation of two fully connected layers from the two CNNs, which fused features, is fed into a softmax classifier to complete the classification. The experimental results demonstrate that the proposed method can provide 98.52%, 99.54% and 99.54% classification accuracy on the Indian Pines, University of Pavia and Salinas data, respectively. And the proposed method can also obtain good performance even with limited training samples.

关键词： Hyperspectral image classification convolutional neural networks deep learning local binary patterns

来源：评论

学校读者我要写书评

暂无评论

Ear recognition using local binary patterns: A comparative experimental study

引用

EXPERT SYSTEMS WITH APPLICATIONS 2019年 118卷 182-200页

作者： Hassaballah, M. Alshazly, Hammam A. Ali, Abdelmgeid A. South Valley Univ Comp Sci Dept Fac Comp & Informat Luxor Egypt South Valley Univ Math Dept Fac Sci Qena 83523 Egypt Minia Univ Comp Sci Dept Fac Comp & Informat Al Minya 61519 Egypt

Identity recognition using local features extracted from ear images has recently attracted a great deal of attention in the intelligent biometric systems community. The rich and reliable information of the human ear and its stable structure over a long period of time present ear recognition technology as an appealing choice for identifying individuals and verifying their identities. This paper considers the ear recognition problem using local binary patterns (LBP) features. Where, the LBP-like features characterize the spatial structure of the image texture based on the assumption that this texture has a pattern and its strength (amplitude)-two locally complementary aspects. Their high discriminative power, invariance to monotonic gray-scale changes and computational efficiency properties make the LBP-like features suitable for the ear recognition problem. Thus, the performance of several recent LBP variants introduced in the literature as feature extraction techniques is investigated to determine how can they be best utilized for ear recognition. To this end, we carry out a comprehensive comparative study on the identification and verification scenarios separately. Besides, a new variant of the traditional LBP operator named averaged local binary patterns (ALBP) is proposed and its ability in representing texture of ear images is compared with the other LBP variants. The ear identification and verification experiments are extensively conducted on five publicly available constrained and unconstrained benchmark ear datasets stressing various imaging conditions;namely IIT Delhi (I), IIT Delhi (II), AMI, WPUT and AWE. The obtained results for both identification and verification indicate that the current LBP texture descriptors are successful feature extraction candidates for ear recognition systems in the case of constrained imaging conditions and can achieve recognition rates reaching up to 99%;while, their performance faces difficulties when the level of distortions

关键词： Intelligent biometrics systems Identity verification Ear recognition local binary patterns Experimental evaluation

来源：评论

学校读者我要写书评

暂无评论

Detection of Modic changes in MR images of spine using local binary patterns

引用

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2019年第1期39卷 17-29页

作者： Athertya, Jiyo S. Kumar, Gurunathan Saravana Govindaraj, Jayaraj IIT Madras Dept Engn Design 3D Imaging & Addit Mfg Lab Chennai Tamil Nadu India Indian Inst Technol Dept Engn Design Room 418 Chennai 600036 Tamil Nadu India Apollo Specialty Hosp Dept Radiol Chennai Tamil Nadu India

Background and objective: With increase in prevalence of lower back pain, fast and reliable computer aided methods for clinical diagnosis associated with the same is needed for improving the healthcare reach. The magnetic resonance images exhibit a change in signal intensity on the vertebral body close to end plates, which are termed as Modic changes (MC), and are known to be clear indicators of lower back pain. The current work deals with computer aided methods for automating the classification of signal changes between normal and degenerate cases so as to aid physicians in precise and suitable diagnosis for the ailment. Methods: In order to detect Modic changes in vertebrae, initially the vertebrae are segmented from sagittal MR T1 and T2 imaged using a semi automatic cellular automata based segmentation. This is followed by textural feature extraction using local binary patterns (LBP) and its variants. Various classifiers based on machine learning approaches using Random Forest, kNN, Bayes and SVM were evaluated for its classification performance. Since medical image dataset in general have bias towards healthy and diseased state, data augmentation techniques were also employed. Results: The implemented method is tested and validated over a dataset containing 100 patients. The proposed framework achieves an accuracy of 81% and 91.7% with and without augmentation of data respectively. A comparative study with the state of art methods reported in literature shows that the method proposed in better in terms of computational cost without any compromise on classification accuracy. Conclusion: A novel approach to identify MC in vertebrae by exploiting textural features is proposed. This shall assist radiologists in detecting abnormalities and in treatment planning. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Modic changes Vertebral degenerations local binary patterns Cellular automata Feature detection Data augmentation

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：