检索结果-内蒙古大学图书馆

Robust Facial Expression Recognition using local Haar Mean binary pattern

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2018年第5期34卷 1237-1249页

作者： Goyani, Mahesh Patel, Narendra Charotar Univ Sci & Technol Dept Comp Engn Changa 388421 India Gujarat Technol Univ Dept Comp Engn Vv Nagar 388120 India

In this paper, we propose a hybrid statistical feature extractor, local Haar Mean binary pattern (LHMBP). It extracts level-1 haar approximation coefficients and computes local Mean binary pattern (LMBP) of it. LMBP code of pixel is obtained by weighting the thresholded neighbor value of 3x3 patch on its mean. LHIVIBP produces highly discriminative code compared to other state of the art methods. To localize appearance features, approximation subband is divided into MxN regions. LHMBP feature descriptor is derived by concatenating LMBP distribution of each region. We also propose a novel template matching strategy called Histogram Normalized Absolute Difference (HNAD) for histogram based feature comparison. Experiments prove the superiority of HNAD over well-known template matching techniques such as L2 norm and Chi-Square. We also investigated LHMBP for expression recognition in low resolution. The performance of the proposed approach is tested on well-known CK, JAFFE, and SFEW facial expression datasets in diverse situations.

关键词： affective computing appearance based feature local binary pattern Gabor filter support vector machine

来源：评论

学校读者我要写书评

暂无评论

Viral Pneumonia Detection from Chest X-rays using GLCM and LBP features

Viral Pneumonia Detection from Chest X-rays using GLCM and L...

引用

2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Jeslin Shah, P.A. Sankar, Deepa Cochin University of Science and Technology Division of Electronics Engineering School of Engineering Kerala Kochi682022 India

ISBN: (纸本)9798350308167

The early contact less detection of viral pneumonia is important as the virus have the ability to mutate and adapt frequently resulting in an epidemic situation or potential pandemic in a short time. This work unveils a technique for identifying viral pneumonia from chest X-rays. A combination of Gray Level Co-occurrence Matrix (GLCM) and local binary pattern (LBP) features with Support Vector Machine (SVM) classifier is used for detection. The effect of various classifiers and feature combinations on viral pneumonia detection are also assessed. From the experimental results, the combination of GLCM and LBP features along with SVM classifier gives the best result with an accuracy of 90.5% and F1 score of 0.9073 compared to stat-of-the-art. © 2023 IEEE.

关键词： Gaussian filtering Gray Level Co-occurrence Matrix local binary pattern Support Vector Machine Viral Pneumonia classification

来源：评论

学校读者我要写书评

暂无评论

An Optimized Image Annotation Method Utilizing Integrating Neural Networks Model and Slantlet Transformation 1

An Optimized Image Annotation Method Utilizing Integrating N...

引用

1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Hashim, Mohammed Mahdi Al-Hilali, Aqeel A. Qasim, Haitham Bashar Salah, Omar Raad Nahi, Abdullah A. Ministry of Higher Education and Scientific Research Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq AL-Esraa University College of Science Department of Computer Science Baghdad Iraq Al-Hadba University College Department of Computer Technology Engineering Mosul Iraq Management Technical College Southern Technical University Department of Information Technologies Basrah Iraq

ISBN: (纸本)9798350342796

The inconceivable volume of online images produced by websites and personal collections has made it difficult to retrieve images from vast databases accurately. Practically, consumers need help to get the relevant information they seek. Humans are able to identify images according to their apparent characteristics. In addition, the image pixels cannot offer enough detail for creating semantic ideas, rendering image annotation a difficult endeavor. A particularly more challenging assignment in Computer Vision (CV) and multimedia studies is image annotation (IA), in which the method assigns keywords from the dictionary to a given digital image. The major aim of IA is to convert the given digital image into single or different labels. To address this issue, this study focused on the extraction of automatic features to improve image annotation. Different techniques have been used with the suggested method such as YCbCr, local binary pattern (LBP), and Slantlet Transform (ST) to improve a Feature Extraction (FE). In addition, the most widely used datasets, such as Flickr8k, ESP-Game, and Corel-5k, were used to test the proposed study. The evaluation results demonstrated superior annotation flexibility, enhanced accuracy, and decreased computation cost. © 2023 IEEE.

关键词： Computer Vision Conventional Neural Network Image annotation local binary pattern Slantlet Transform

来源：评论

学校读者我要写书评

暂无评论

Novel Synchronous Brain Computer Interface Based on 2-D EEG local binary patterning

Novel Synchronous Brain Computer Interface Based on 2-D EEG ...

引用

Intelligent Systems Conference (IntelliSys)

作者： De Venuto, Daniela Mezzina, Giovanni Politecn Bari Dept Elect & Informat Engn Via E Orabona 4 I-70125 Bari Italy

ISBN: (纸本)9783030295134;9783030295127

This paper proposes the design and the validation through in-vivo measurements, of an innovative machine learning (ML) approach for a synchronous Brain Computer Interface (BCI). The here-proposed system analyzes EEG signals from 8 wireless smart electrodes, placed in motor, and sensory-motor cortex area. For its functioning, the BCI exploits a specific brain activity patterns (BAP) elicited during the measurements by using clinical-inspired stimulation protocol that is suitable for the evocation of the Movement-Related Cortical Potentials (MRCPs). The proposed BCI analyzes the EEGs through symbolization-based algorithm: the local binary patterning, which - due to its end-to-end binary nature - strongly reduces the computational complexity of the features extraction (FE) and real-time classification stages. As last step, the user intentions discrimination is entrusted to a weighted Support Vector Machine (wSVM) with linear kernel. The data have been collected from 3 subjects (aged 26 +/- 1), creating an overall dataset that consists of 391 +/- 106 observations per participant. The in-vivo real-time validation showed an intention recognition accuracy of 85.61 +/- 1.19%. The overall computing chain requests, on average, just 3 ms beyond the storage time.

关键词： BCI EEG Symbolization local binary pattern Machine learning

来源：评论

学校读者我要写书评

暂无评论

Improved spatiotemporal local monogenic binary pattern for emotion recognition in the wild 14

Improved spatiotemporal local monogenic binary pattern for e...

引用

16th ACM International Conference on Multimodal Interaction, ICMI 2014

作者： Huang, Xiaohua He, Qiuhai Hong, Xiaopeng Zhao, Guoying Pietikänen, Matti Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu Finland

ISBN: (纸本)9781450328852

local binary pattern from three orthogonal planes (LBPTOP) has been widely used in emotion recognition in the wild. However, it suffers from illumination and pose changes. This paper mainly focuses on the robustness of LBP-TOP to unconstrained environment. Recent proposed method, spatiotemporal local monogenic binary pattern (STLMBP) [14], was verified to work promisingly in different illumination conditions. Thus this paper proposes an improved spatiotemporal feature descriptor based on STLMBP. The improved descriptor uses not only magnitude and orientation, but also the phase information, which provide complementary information. In detail, the magnitude, orientation and phase images are obtained by using an effective monogenic filter, and multiple feature vectors are finally fused by multiple kernel learning. STLMBP and the proposed method are evaluated in the Acted Facial Expression in the Wild as part of the 2014 Emotion Recognition in the Wild Challenge. They achieve competitive results, with an accuracy gain of 6.35% and 7.65% above the challenge baseline (LBP-TOP) over video. Copyright 2014 ACM.

关键词： local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Detecting Fake Face Images in Biometric Systems Using LBP Feature Extraction and CNN 6th

Detecting Fake Face Images in Biometric Systems Using LBP Fe...

引用

6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Gowri Sree Lakshmi, N. Chaturya Sri, K. Kusuma Kumari, R. Sobha Rani, K. Chandra Sekhar Reddy, N. Department of Computer Science and Engineering Godavari Institute of Engineering and Technology Andhra Pradesh Rajamahendravaram India Department of Computer Science and Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Tamil Nadu Chennai 600095 India

ISBN: (纸本)9789819778799

Face spoofing has become an increasing concern in terms of security. It involves using misleading face images or videos to trick authentication systems. This study uses convolutional neural network (CNN) and local binary patterns (LBP) approaches for the extraction of features to propose a reliable solution for detecting face spoofing. It utilizes LBP to extract unique features from facial pictures. A CNN model that has undergone substantial training with a sizable dataset is then fed these extracted features. The purpose of this training is to distinguish between genuine and fake faces. CNN’s ability to automatically learn advanced features enables it to effectively identify subtle differences between real and counterfeit faces. Moreover, the implications of this research extend to various domains such as mobile banking, access control, and surveillance. This development marks an important milestone in safeguarding against deceptive practices and reinforcing the trustworthiness of face-based authentication mechanisms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural network Deep texture feature extraction Face faking Face recognition Face spoofing detection local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Diabetic retinopathy detection using local ternary pattern

引用

INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2020年第4期34卷 334-353页

作者： Anitha, A. Maheswari, S. Uma Govt Coll Technol Dept Elect & Commun Engn Coimbatore 641013 Tamil Nadu India Coimbatore Inst Technol Dept Elect & Commun Engn Coimbatore 641014 Tamil Nadu India

An intelligent way of diabetic retinopathy detection (DR) at an early stage is required to prevent blindness. DR is detected by analysing the retinal background without segmenting the lesions. This work focuses on local ternary pattern (LTP) for analysing texture of the fundus image. As local binary pattern (LBP) is more sensitive to noise and illumination variation, LTP is employed and its discriminative power is explored. LTP is obtained for all three colour components, red (R), green (G) and blue (B) for different radius considering eight neighbours. The histogram of LTP and variance form a feature set for the classifiers KNN and random forest with ten-fold cross validation. Random forest provides a sensitivity and specificity of 100%. The average sensitivity and specificity of nearly 91% are achieved. The proposed algorithm is very fast and can be used as a screening test for retinal abnormalities detection.

关键词： local ternary pattern LTP local binary pattern LBP random forest computer aided diagnosis fundus images K-nearest neighbourhood KNN diabetic retinopathy

来源：评论

学校读者我要写书评

暂无评论

OPENCV Based Smart Attendance System Using Facial Recognition 4

OPENCV Based Smart Attendance System Using Facial Recognitio...

引用

4th International Conference for Emerging Technology, INCET 2023

作者： Tirupal, T. Kumar, M. Naveen Basha, P. Mahaboob Babu, J. Mahesh Rathan, O. G.Pullaiah College of Engineering and Technology Department of ECE Andhra Pradesh Kurnool India

ISBN: (纸本)9798350335750

Face is a very unique part of human body which consists unique features for every individual who has distinguishes an individual is their face. The facial recognition system can be developed by using facial landmarks or facial nodal points on the face. How ever the attendance taken by old traditional method in paper or attendance book register was a time taking process and also storing and fetching data was difficult and it will take lot of human power. But the OpenCV module and local binary pattern Histogram (LBPH) take the picture of the student and mark the attendance and attendance marked was stored in excel sheet. © 2023 IEEE.

关键词： local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Decision Support System in Identification of Lesions in the Dense Breast on Digital Mammograms 2

Decision Support System in Identification of Lesions in the ...

引用

2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Amin, Ashwini Dinesh Acharya, U. Prakashini, K. Siddalingaswamy, P.C. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Radio Diagnosis Kasturba Medical Hospital Manipal Academy of Higher Education Manipal India

Background The most common cancer affecting women globally is breast cancer. The most effective and extensively used tool for identifying breast abnormalities in the early stage is mammography screening. However, it is less effective when there is high breast density. Therefore, the radiologist considers a sonomammography in addition to mammography to identify and characterize the lesion. Therefore, we aim at modelling a decision support system to identify a lesion in a dense breast using a mammogram and thereby avoiding additional sonomammogram. Methods In this work, the image pixels were pre-processed to produce super-pixels that are very meaningful and easy to analyse. Texture features extracted using a rotation invariant local binary pattern (RI-LBP) approach from super-pixels. Feature Selection algorithm was used to extract the top 5 features. The performance of various machine learning models was studied, and the best model was used for the prediction of the presence or absence of lesions in the mammogram images. Results The findings suggest that breast masses localized in the dense background can be described by features produced from RI-LBP super-pixel patterns in very effective and efficient ways. The experiment showed that with 500 super-pixels and only 5 RI-LBP features, the Support Vector machine model with gaussian kernel yielded an accuracy value of 90.38% and an area under the receiver operating characteristic (ROC) curve score of 0.96 for the mammogram dataset. © Published under licence by IOP Publishing Ltd.

关键词： local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Face anti-spoofing based on ASFF and LBP self-supervision branch 3

Face anti-spoofing based on ASFF and LBP self-supervision br...

引用

3rd International Conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Ren, Junjie Zhang, Yi Zhang, Ruisheng Luo, Jianfeng Whale Cloud Technology Co. Ltd Nanjing210000 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China

ISBN: (纸本)9781510667563

Since the tremendous advancement of face recognition technology, how to detect real human faces from images has gradually captured the attention of researchers. To address the problem that most face anti-spoofing approaches ignore context information and local information about faces, this paper proposes a face anti-spoofing algorithm that fuses multi-scale features and texture features of images. On the one hand, we extract multi-scale features using adaptively spatial feature fusion (ASFF) and polarized self-attention (PSA) mechanism. On the other hand, local binary patterns (LBP) is used to form an auxiliary self-supervision branch to extract image texture difference information. As a final step, end-to-end live detection is achieved by balancing the difference between two different loss functions based on homoscedastic uncertainty. The EER of the presented method is 0.81% and 1.26% on CASIA-FASD and MSU-MFSD, respectively, and the ACER is only 1.64% and 2.07%±1.05% on the second and third protocols of Oulu_NPU, which indicates the validity of the method. © 2023 SPIE.

关键词： local binary pattern

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：