检索结果-内蒙古大学图书馆

Unveiling encephalopathy signatures: A deep learning approach with locality-preserving features and hybrid neural network for EEG analysis

引用

NEUROSCIENCE LETTERS 2025年 849卷 138146页

作者： Jacob, Jisu Elsa Chandrasekharan, Sreejith Iype, Thomas Cherian, Ajith Sree Chitra Thirunal Coll Engn Dept Elect & Commun Engn Thiruvananthapuram Kerala India Govt Med Coll Dept Neurol Thiruvananthapuram Kerala India SCTIMST Dept Neurol Thiruvananthapuram Kerala India

EEG signals exhibit spatio-temporal characteristics due to the neural activity dispersion in space over the brain and the dynamic temporal patterns of electrical activity in neurons. This study tries to effectively utilize the spatio-temporal nature of EEG signals for diagnosing encephalopathy using a combination of novel locality preserving feature extraction using local binary patterns (LBP) and a custom fine-tuned Long Short-Term Memory (LSTM) neural network. A carefully curated primary EEG dataset is used to assess the effectiveness of the technique for treatment of encephalopathies. EEG signals of all electrodes are mapped onto a spatial matrix from which the custom feature extraction method isolates spatial features of the signals. These spatial features are further given to the neural network, which learns to combine the spatial information with temporal dynamics summarizing pertinent details from the raw EEG data. Such a unified representation is key to perform reliable disease classification at the output layer of the neural network, leading to a robust classification system, potentially providing improved diagnosis and treatment. The proposed method shows promising potential for enhancing the automated diagnosis of encephalopathy, with a remarkable accuracy rate of 90.5%. To the best of our knowledge, this is the first attempt to compress and represent both spatial and temporal features into a single vector for encephalopathy detection, simplifying visual diagnosis and providing a robust feature for automated predictions. This advancement holds significant promise for ensuring early detection and intervention strategies in the clinical environment, which in turn enhances patient care.

关键词： Electroencephalogram local binary pattern Recurrent neural networks Encephalopathy Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Spatial and frequency domain-based feature fusion for accurate detection of schizophrenia using AI-driven approaches

引用

HEALTH INFORMATION SCIENCE AND SYSTEMS 2025年第1期13卷 1-20页

作者： Tyagi, Ashima Singh, Vibhav Prakash Gore, Manoj Madhava Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Prayagraj 211004 India

Schizophrenia is a neuropsychiatric disorder that hampers brain functions and causes hallucinations, delusions, and bizarre behavior. The stigmatization associated with this disabling disorder drives the need to build diagnostic models with impeccable performances. Neuroimaging modality such as structural MRI is coupled with machine learning techniques to perform schizophrenia diagnosis with increased reliability. We investigate the structural aberrations present in the structural MR images using machine learning techniques. In this study, we propose a new hybrid approach using spatial and frequency domain-based features for the early automated detection of schizophrenia using machine learning techniques. The spatial or texture features are extracted using the local binary pattern method, and frequency-based features, including magnitude and phase, are extracted using the fast fourier transform feature extraction technique. Hybrid features, combining spatial and frequency-based features, are utilized for schizophrenia classification using support vector machine, random forest, and k-nearest neighbor with stratified 10-fold cross-validation. The support vector machine and random forest classifiers achieve encouraging detection performances on the hybrid feature set, with 86.5% and 85.1% accuracy, respectively. Among the three classifiers, k-nearest neighbor shows outstanding detection performance with an accuracy of 98.1%. The precision and recall achieved by the k-nearest neighbor classifier are 98.1% and 98.0% respectively, reflecting accurate detection of schizophrenia by the proposed model.

关键词： Schizophrenia Structural MRI Adaptive median filter local binary pattern Fast Fourier transform Machine learning

来源：评论

学校读者我要写书评

暂无评论

LBPMobileNet-based novel and simple leather image classification method

引用

Neural Computing and Applications 2025年 1-18页

作者： Varghese, Anjli Jawahar, Malathy Prince, A. Amalin Gandomi, Amir H. Department of Electrical and Electronics Engineering BITS Pilani K K Birla Goa Campus Goa Sancoale403726 India Central Leather Research Institute Chennai600020 India Faculty of Engineering & IT University of Technology Sydney 123 Broadway SydneyNSW2007 Australia Obuda University Budapest1034 Hungary

This article presents the design of a robust leather species identification technique. It aims to intertwine deep learning with leather image analysis. Hence, this work collects and analyzes large-scale leather image data for diverse learning. The data involve 7600 unique images with species-distinct and varied pore patterns from four species. It proposes a novel dual-stream architecture for accurate leather image classification. It is a fusion of local binary pattern-based texture analysis and MobileNet-based adaptive feature learning, hence the name LBPMobileNet. The former highlights the local structural pattern of an image, and the latter efficiently learns the species’ uniqueness. The dual-stream model analyzes two sources of images to provide more reliable and robust learning from different textured images. At the same time, it adopts two MobileNets to design a computationally efficient model. Thus, the proposed model utilizes limited resources and provides 96.45% accurate leather image classification. Further, the performance analysis affirms the generalization ability of the proposed model by predicting species from leather images with ideal and complex behavior. It also validates the robustness and computational efficiency of the proposed model with the state-of-the-art deep learning models. Thus, this study proves the relevance of local binary patterns, fused feature analysis, dual-stream architecture, and deep learning for efficient leather image analysis. It, thereby, assists the leather experts by developing an automatic and accurate species prediction method. © The Author(s) 2025.

关键词： local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Multimodal biometrics authentication using extreme learning machine with feature reduction by adaptive particle swarm optimization

引用

VISUAL COMPUTER 2024年第3期40卷 1383-1394页

作者： Vensila, C. Wesley, A. Boyed Marthandam Manonmaniam Sundaranar Univ Christian Coll Dept Comp Sci Nesamony Mem Tirunelveli 627012 Tamil Nadu India Marthandam Manonmaniam Sundaranar Univ Nesamony Mem Christian Coll Dept PG Comp Sci Tirunelveli 627012 Tamil Nadu India

The necessity for personal data protection has become increasingly critical in recent years. The single trait-based authentication fails in many cases and hence multimodal biometric traits are utilized in this work. This research work focuses on the utilization of multimodal biometric traits for authentication. The face, fingerprint, and finger vein traits are considered in this work, feature extraction was done by local binary pattern and for the optimization of features, adaptive particle swarm optimization was employed. The extreme learning machine was proposed here for the classification and proficient results were generated. For the iteration count of 40 and population count of 30 of the optimization algorithm, the authentication model sensitivity and specificity are 95.69 and 94.29%. The outcome of this research work paves the way toward proficient security authentication.

关键词： Biometrics Particle swarm optimization local binary pattern Extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

local Extrema Min-Max pattern: A novel descriptor for extracting compact and discrete features for hand gesture recognition

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 93卷

作者： Bahuguna, Arti Bhaumik, Gopa Govil, Mahesh Chandra Natl Inst Technol Sikkim Comp Sci & Engn Ravangla 737139 Sikkim India Natl Inst Technol Jamshedpur Comp Sci & Engn Jamshedpur 831014 Jharkhand India

In this paper, a handcrafted feature descriptor namely local Extrema Min-Max pattern (LEMMP) is introduced for static hand gesture recognition (HGR). LEMMP thoroughly characterizes the discriminative information between a specific coefficient and its nearby neighbors within a local window. The proposed approach extracts 2nd -order local information by encoding the most informative directions contained within multiple discrete spatial associations from each neighborhood pixel using a two -structure encoding method, which helps in detecting the gray level variations that may occur in different directions. LEMMP encodes the structure of hand gestures utilizing texture information in an easy -to -understand and compact coding scheme, resulting in improved accuracy with less memory and time as compared to existing approaches. Furthermore, the features extracted by the proposed LEMMP are classified using SVM classifier. The proposed LEMMP is tested on nine benchmark HGR datasets. The experimental results and the visual analysis demonstrate that the proposed LEMMP outperforms the existing state-of-the-art approaches with an accuracy of 52%(ASL Static), 92% (MUGD Set1), 98% (MUGD Set2), 75%(MUGD Set3), 64%(MUGD Set4), 71%(MUGD Set5), 98%(ASL Digit), 85% (NUSI Dataset), 80% (NUS -II Dataset), 99% (ASLFS A), 99% (ASLFS B), 99%(ASLFS C), 96%(ASLFS D), 99%(ASLFS E), 39% Bengali Sign Language, 70% (HG -14 Dataset) and 42% (OU Hands) respectively.

关键词： Hand gesture recognition local binary pattern Non-verbal interaction Feature extraction SVM classifier LEMMP

来源：评论

学校读者我要写书评

暂无评论

local binary pattern (LBP) and local phase quantization (LBQ) based on Gabor filter for face representation

引用

NEUROCOMPUTING 2013年 116卷 260-264页

作者： Zhou, Shu-Ren Yin, Jian-Ping Zhang, Jian-Ming Natl Univ Def Technol Postdoctoral Stn Comp Sch Changsha 410073 Hunan Peoples R China Changsha Univ Sci & Technol Comp & Commun Engn Sch Changsha 410004 Hunan Peoples R China

Sometimes realistic face representation is confronted with blur or low-resolution face images, as a result, existing classification methods are not powerful and robust enough. This paper proposes a novel face representation approach (GLL) which fuses Gabor filter, local binary pattern (LBP) and local Phase Quantization (LPQ). In the process of Gabor filter, it uses Gabor wavelet functions with two scales and eight orientations to capture the salient visual properties in face image. On this basis of Gabor features, we acquire LBP features and LPQ features, respectively, so as to fully explore the blur invariant property and the information in the spatial domain and among different scales and orientations. Experiments on both CMU-PIE and Yale B demonstrate the effectiveness of our GLL when dealing with different condition face data sets. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Face representation Gabor filter local binary pattern local phase quantization Feature fusion

来源：评论

学校读者我要写书评

暂无评论

Information fusion technology for terahertz spectra and hyperspectral imaging in wood species identification

引用

EUROPEAN JOURNAL OF WOOD AND WOOD PRODUCTS 2024年第3期82卷 579-589页

作者： Wang, Yuan He, Yihao Wang, Zhigang Avramidis, Stavros Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Beijing Forestry Univ Res Ctr Biodivers Intelligent Monitoring Beijing 100083 Peoples R China Beijing Forestry Univ Joint Int Res Inst Wood Nondestruct Testing & Eval Beijing 100083 Peoples R China Univ British Columbia Fac Forestry Dept Wood Sci Vancouver BC V6T1Z4 Canada

This paper proposes the use of information fusion technology to identify different wood species by combining spectral and spatial information from hyperspectral images and terahertz (THz) spectra. The study utilized five species of coniferous wood as experimental samples. The hyperspectral and terahertz raw images and spectra acquired by the spectroscopic instruments were preprocessed using standard normal variational transform (SNV). Three methods, namely, competitive adaptive reweighting (CARS), uninformative variable elimination (UVE), and random frog hopping (RF), were employed to select relevant frequency features in both hyperspectral image spectral information and THz spectra. For hyperspectral image spatial information, three algorithms, grayscale co-occurrence matrix (GLCM), local binary pattern (LBP), and Gaussian Markov random field (GMRF) were used to extract texture features. Subsequently, these three sets of extracted features were recognized separately using an extreme learning machine (ELM) model. The results showed that the accuracies achieved by the three features alone in wood identification were 71.8% for the spectral information, 85% for the hyperspectral image spatial information, and 91.7% for THz spectra. However, there was still room for improvement in terms of accuracy. Consequently, the study fused the hyperspectral image spectral and spatial information with THz spectral information, and the ELM model was employed to recognize the fused data. The results indicated that this fusion method led to a substantial enhancement in wood identification accuracy, achieving an impressive 96.7%. This accuracy markedly surpassed the highest recognition accuracy achieved by a single information feature.

关键词： local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Multi-Feature Object Tracking with Fusion of LBP and Attention Based on Siamese Networks

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer Science 2025年第1期52卷 208-222页

作者： Hu, Zhangfang Yuan, Yuan Wang, Yi Wang, Bokun Key Laboratory of Optical Information Sensing and Technology School of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

Siamese networks utilize deep learning models to achieve a balance between tracking speed and accuracy in visual object tracking. However, in low light and other challenging lighting conditions, the contours and textures of the tracked object may not be accurately represented in the feature maps. This can result in blurriness and interference from similar distractions, as non-edge features may be overlooked. To tackle these challenges, this paper proposes a multi-feature object tracking approach that combines Siamese networks with local binary patterns (LBP) and attention mechanisms. After preprocessing the video frames for LBP feature extraction, the resulting local binary pattern maps are input into a SiamRPN network, with ResNeSt as its backbone. The incorporation of local binary patterns enhances feature representation by capturing texture information from the target's appearance. Additionally, a coordinated attention mechanism is applied after each feature layer of the network to further improve tracking accuracy. This mechanism dynamically adjusts the weights of different features to optimize performance across various scenarios. The target's position is then determined by merging the deep and shallow features retrieved by the network, which are subsequently fed into the RPN network for regression classification. Experimental results demonstrate the effectiveness of the proposed algorithm in tracking objects. © (2025), (International Association of Engineers). 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

Modified local binary pattern for human face recognition based on sparse representation

引用

International Journal of Computers and Applications 2014年第2期36卷 64-71页

作者： Mawloud, Guermoui Djamel, Melaab Department of Electronics Faculty of Technology University of Batna Batna 05000 Algeria

Over the last decades, research on facial analysis has witnessed a growing interest and became a very active topic in computer vision. Broadly, it can be addressed in either of the two ways, namely facial representation and classification. Considering the former category, many representations could be found in the literature. One of the most popular representations is the well-known local binary pattern (LBP). In this respect, we propose in this paper a novel alternative to the basic LBP for face representation, termed a modified local binary pattern (MLBP), which we prove its outperformance over other popular techniques. On the other hand, we exploit the sparsity of the representative set of MLBPs for recognizing different face classes. Therefore, compressive sensing theory was employed to construct a so-called sparse representation classifier. Experimental results conducted on three popular face databases pointed out the superiority of our proposed strategy over other state-of-the-art techniques.

关键词： local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Combined local pattern (CLP): A Novel Descriptor for Face Recognition 4

Combined Local Pattern (CLP): A Novel Descriptor for Face Re...

引用

4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Karanwal, Shekhar Cse Department UK Dehradun India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

LBP is renowned as most powerful texture descriptor. But major issue which LBP possesses is the noisy thresholding function. This sacrifices the discriminativity of the descriptor. To complement that three LBP variants are introduced and these are Mean LBP (MLBP), Median LBP (MnLBP) and Combined local pattern (CLP). In MLBP, the mean of the whole patch (3×3) is used for neighborhoods comparison. In MnLBP, the median of whole patch (3×3) is used for neighborhood comparison. The Mean and Median value proves out very effective in contrast to LBP, in which center pixel is used for the neighborhoods comparison. Both MLBP and MnLBP comprehensively conquer the performance of the LBP. To build more productive descriptor features of LBP, MLBP and MnLBP are integrated into one framework. This joined descriptor is called as CLP. The CLP conquer the results of all alone descriptors. Additionally it outrun the performance of the various literature methods. The compression in feature size is accomplished by PCA and classification is assisted from SVMs. Experiments are conducted on ORL and GT face datasets. © 2022 IEEE.

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：