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Traffic sign recognition algorithm based on shape signature and dual-tree complex wavelet transform 被引量:8
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作者 蔡自兴 谷明琴 《Journal of Central South University》 SCIE EI CAS 2013年第2期433-439,共7页
A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. S... A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate. 展开更多
关键词 traffic sign recognition signATURE DT-CWT 2DICA nearest neighbor classifier
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Traffic Sign Recognition Based on CNN and Twin Support Vector Machine Hybrid Model
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作者 Yang Sun Longwei Chen 《Journal of Applied Mathematics and Physics》 2021年第12期3122-3142,共21页
With the progress of deep learning research, convolutional neural networks have become the most important method in feature extraction. How to effectively classify and recognize the extracted features will directly af... With the progress of deep learning research, convolutional neural networks have become the most important method in feature extraction. How to effectively classify and recognize the extracted features will directly affect the performance of the entire network. Traditional processing methods include classification models such as fully connected network models and support vector machines. In order to solve the problem that the traditional convolutional neural network is prone to over-fitting for the classification of small samples, a CNN-TWSVM hybrid model was proposed by fusing the twin support vector machine (TWSVM) with higher computational efficiency as the CNN classifier, and it was applied to the traffic sign recognition task. In order to improve the generalization ability of the model, the wavelet kernel function is introduced to deal with the nonlinear classification task. The method uses the network initialized from the ImageNet dataset to fine-tune the specific domain and intercept the inner layer of the network to extract the high abstract features of the traffic sign image. Finally, the TWSVM based on wavelet kernel function is used to identify the traffic signs, so as to effectively solve the over-fitting problem of traffic signs classification. On GTSRB and BELGIUMTS datasets, the validity and generalization ability of the improved model is verified by comparing with different kernel functions and different SVM classifiers. 展开更多
关键词 CNN Twin Support Vector Machine Wavelet Kernel Function Traffic sign recognition Transfer Learning
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CNN-based Traffic Sign Recognition
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作者 Qingkun Huang Askar Mijiti 《计算机科学与技术汇刊(中英文版)》 2022年第1期1-7,共7页
Background:The rapid development of the automobile industry has led to an increase in the output and holdings of automobiles year by year,which has brought huge challenges to the current traffic management.Method:This... Background:The rapid development of the automobile industry has led to an increase in the output and holdings of automobiles year by year,which has brought huge challenges to the current traffic management.Method:This paper adopts a traffic sign recognition technology based on deep convolution neural network(CNN):step 1,preprocess the collected traffic sign images through gray processing and near interpolation;step 2,automatically extract image features through the convolutional layer and the pooling layer;step 3,recognize traffic signs through the fully connected layer and the Dropout technology.Purpose:Artificial intelligence technology is applied to traffic management to better realize intelligent traffic assisted driving.Results:This paper adopts an Adam optimization algorithm for calculating the loss value.The average accuracy of the experimental classification is 98.87%.Compared with the traditional gradient descent algorithm,the experimental model can quickly converge in a few iteration cycles. 展开更多
关键词 Traffic sign recognition Convolution Neural Network(CNN) Adam Algorithm
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Traffic sign recognition based on subspace
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作者 张志佳 HE Chun-jing +1 位作者 Li Yuan Yuan LI Wen-qiang 《Journal of Chongqing University》 CAS 2016年第2期52-60,共9页
The features extracted by principle component analysis(PCA) are the best descriptive and the features extracted by linear discriminant analysis(LDA) are the most classifiable. In this paper, these two methods are comb... The features extracted by principle component analysis(PCA) are the best descriptive and the features extracted by linear discriminant analysis(LDA) are the most classifiable. In this paper, these two methods are combined and a PC-LDA approach is used to extract the features of traffic signs. After obtaining the binary images of the traffic signs through normalization and binarization, PC-LDA can extract the feature subspace of the traffic sign images with the best description and classification. The extracted features are recognized by using the minimum distance classifier. The approach is verified by using MPEG7 CE Shape-1 Part-B computer shape library and traffic sign image library which includes both standard and natural traffic signs. The results show that under the condition that the traffic sign is in a nature scene, PC-LDA approach applied to binary images in which shape features are extracted can obtain better results. 展开更多
关键词 principle component analysis principle component-linear discriminant analysis feature extracting recognition of traffic sign
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Recent Advances on Deep Learning for Sign Language Recognition
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作者 Yanqiong Zhang Xianwei Jiang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第6期2399-2450,共52页
Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automa... Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automatically recognizing and interpreting sign language gestures,has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world.The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR.This paper presents a comprehensive and up-to-date analysis of the advancements,challenges,and opportunities in deep learning-based sign language recognition,focusing on the past five years of research.We explore various aspects of SLR,including sign data acquisition technologies,sign language datasets,evaluation methods,and different types of neural networks.Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)have shown promising results in fingerspelling and isolated sign recognition.However,the continuous nature of sign language poses challenges,leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition(CSLR).Despite significant advancements,several challenges remain in the field of SLR.These challenges include expanding sign language datasets,achieving user independence in recognition systems,exploring different input modalities,effectively fusing features,modeling co-articulation,and improving semantic and syntactic understanding.Additionally,developing lightweight network architectures for mobile applications is crucial for practical implementation.By addressing these challenges,we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community. 展开更多
关键词 sign language recognition deep learning artificial intelligence computer vision gesture recognition
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A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence
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作者 Xianwei Jiang Yanqiong Zhang +1 位作者 Juan Lei Yudong Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期1-40,共40页
Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign La... Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing. 展开更多
关键词 Chinese sign Language recognition deep neural networks artificial intelligence transfer learning hybrid network models
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Multi-scale context-aware network for continuous sign language recognition
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作者 Senhua XUE Liqing GAO +1 位作者 Liang WAN Wei FENG 《虚拟现实与智能硬件(中英文)》 EI 2024年第4期323-337,共15页
The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand an... The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information.In addition,the signs have different lengths,whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling,which disturbs the perception of complete signs.In this study,we propose a Multi-Scale Context-Aware network(MSCA-Net)to solve the aforementioned problems.Our MSCA-Net contains two main modules:(1)Multi-Scale Motion Attention(MSMA),which uses the differences among frames to perceive information of the hands and face in multiple spatial scales,replacing the heavy feature extractors;and(2)Multi-Scale Temporal Modeling(MSTM),which explores crucial temporal information in the sign language video from different temporal scales.We conduct extensive experiments using three widely used sign language datasets,i.e.,RWTH-PHOENIX-Weather-2014,RWTH-PHOENIX-Weather-2014T,and CSL-Daily.The proposed MSCA-Net achieve state-of-the-art performance,demonstrating the effectiveness of our approach. 展开更多
关键词 Continuous sign language recognition Multi-scale motion attention Multi-scale temporal modeling
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A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation 被引量:1
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作者 Mohamed Karray Nesrine Triki Mohamed Ksantini 《Computers, Materials & Continua》 SCIE EI 2024年第7期119-138,共20页
Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recogn... Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology. 展开更多
关键词 Driving automation advanced driver assistance systems(ADAS) traffic sign recognition(TSR) artificial intelligence ensemble learning belief functions voting method
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Continuous Sign Language Recognition Based on Spatial-Temporal Graph Attention Network 被引量:2
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作者 Qi Guo Shujun Zhang Hui Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第3期1653-1670,共18页
Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtempora... Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtemporal graph attention network to focus on essential features of video series.The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatialtemporal graph to reflect inter-frame relevance and physical connections between nodes.The graph-based multihead attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration,and short-term motion correlation modeling is completed via a temporal convolutional network.We adopted BLSTM to learn the long-termdependence and connectionist temporal classification to align the word-level sequences.The proposed method achieves competitive results regarding word error rates(1.59%)on the Chinese Sign Language dataset and the mean Jaccard Index(65.78%)on the ChaLearn LAP Continuous Gesture Dataset. 展开更多
关键词 Continuous sign language recognition graph attention network bidirectional long short-term memory connectionist temporal classification
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Rotation,Translation and Scale Invariant Sign Word Recognition Using Deep Learning 被引量:2
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作者 Abu Saleh Musa Miah Jungpil Shin +2 位作者 Md.Al Mehedi Hasan Md Abdur Rahim Yuichi Okuyama 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2521-2536,共16页
Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task.One of the main functions of sign language is to communicate with each o... Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task.One of the main functions of sign language is to communicate with each other through hand gestures.Recognition of hand gestures has become an important challenge for the recognition of sign language.There are many existing models that can produce a good accuracy,but if the model test with rotated or translated images,they may face some difficulties to make good performance accuracy.To resolve these challenges of hand gesture recognition,we proposed a Rotation,Translation and Scale-invariant sign word recognition system using a convolu-tional neural network(CNN).We have followed three steps in our work:rotated,translated and scaled(RTS)version dataset generation,gesture segmentation,and sign word classification.Firstly,we have enlarged a benchmark dataset of 20 sign words by making different amounts of Rotation,Translation and Scale of the ori-ginal images to create the RTS version dataset.Then we have applied the gesture segmentation technique.The segmentation consists of three levels,i)Otsu Thresholding with YCbCr,ii)Morphological analysis:dilation through opening morphology and iii)Watershed algorithm.Finally,our designed CNN model has been trained to classify the hand gesture as well as the sign word.Our model has been evaluated using the twenty sign word dataset,five sign word dataset and the RTS version of these datasets.We achieved 99.30%accuracy from the twenty sign word dataset evaluation,99.10%accuracy from the RTS version of the twenty sign word evolution,100%accuracy from thefive sign word dataset evaluation,and 98.00%accuracy from the RTS versionfive sign word dataset evolution.Furthermore,the influence of our model exists in competitive results with state-of-the-art methods in sign word recognition. 展开更多
关键词 sign word recognition convolution neural network(cnn) rotation translation and scaling(rts) otsu segmentation
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Deep Learning-Based Sign Language Recognition for Hearing and Speaking Impaired People
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作者 Mrim M.Alnfiai 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期1653-1669,共17页
Sign language is mainly utilized in communication with people who have hearing disabilities.Sign language is used to communicate with people hav-ing developmental impairments who have some or no interaction skills.The... Sign language is mainly utilized in communication with people who have hearing disabilities.Sign language is used to communicate with people hav-ing developmental impairments who have some or no interaction skills.The inter-action via Sign language becomes a fruitful means of communication for hearing and speech impaired persons.A Hand gesture recognition systemfinds helpful for deaf and dumb people by making use of human computer interface(HCI)and convolutional neural networks(CNN)for identifying the static indications of Indian Sign Language(ISL).This study introduces a shark smell optimization with deep learning based automated sign language recognition(SSODL-ASLR)model for hearing and speaking impaired people.The presented SSODL-ASLR technique majorly concentrates on the recognition and classification of sign lan-guage provided by deaf and dumb people.The presented SSODL-ASLR model encompasses a two stage process namely sign language detection and sign lan-guage classification.In thefirst stage,the Mask Region based Convolution Neural Network(Mask RCNN)model is exploited for sign language recognition.Sec-ondly,SSO algorithm with soft margin support vector machine(SM-SVM)model can be utilized for sign language classification.To assure the enhanced classifica-tion performance of the SSODL-ASLR model,a brief set of simulations was car-ried out.The extensive results portrayed the supremacy of the SSODL-ASLR model over other techniques. 展开更多
关键词 sign language recognition deep learning shark smell optimization mask rcnn model disabled people
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Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare
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作者 Khursheed Aurangzeb Khalid Javeed +3 位作者 Musaed Alhussein Imad Rida Syed Irtaza Haider Anubha Parashar 《Computers, Materials & Continua》 SCIE EI 2024年第1期127-144,共18页
Hand gestures have been used as a significant mode of communication since the advent of human civilization.By facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seaml... Hand gestures have been used as a significant mode of communication since the advent of human civilization.By facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free HCI.HGRoc technology is pivotal in healthcare and communication for the deaf community.Despite significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature extraction.Therefore,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and scalability.Additionally,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer interaction.The proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,respectively.These results demonstrate the effectiveness of CNN as a promising HGRoc approach.The findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics. 展开更多
关键词 Computer vision deep learning gait recognition sign language recognition machine learning
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Medical Sign Recognition of Lung Nodules Based on Image Retrieval with Semantic Features and Supervised Hashing 被引量:1
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作者 Juan-Juan Zhao Ling Pan +1 位作者 Peng-Fei Zhao Kiao-Xian Tang 《Journal of Computer Science & Technology》 SCIE EI CSCD 2017年第3期457-469,共13页
Sign recognition is important for identifying benign and malignant nodules. This paper proposes a new sign recognition method based on image retrieval for lung nodules. First, we construct a deep learning framework to... Sign recognition is important for identifying benign and malignant nodules. This paper proposes a new sign recognition method based on image retrieval for lung nodules. First, we construct a deep learning framework to extract semantic features that can effectively represent sign information. Second, we translate the high-dimensional image features into compact binary codes with principal component analysis (PCA) and supervised hashing. Third, we retrieve similar lung nodule images with the presented adaptive-weighted similarity calculation method. Finally, we recognize nodule signs from the retrieval results, which can also provide decision support for diagnosis of lung lesions. The proposed method is validated on the publicly available databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and lung computed tomography (CT) imaging signs (LISS). The experimental results demonstrate our retrieval method substantially improves retrieval performance compared with those using traditional Hamming distance, and the retrieval precision can achieve 87.29%when the length of hash code is 48 bits. The entire recognition rate on the basis of the retrieval results can achieve 93.52%. Moreover, our method is also effective for real-life diagnosis data. 展开更多
关键词 lung nodule medical sign recognition image retrieval supervised hashing adaptive weight
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New method for recognition of circular traffic sign based on radial symmetry and pseudo-zernike moments 被引量:1
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作者 付梦印 黄源水 马宏宾 《Journal of Beijing Institute of Technology》 EI CAS 2011年第4期520-526,共7页
Recognizing various traffic signs,especially the popular circular traffic signs,is an essential task for implementing advanced driver assistance system.To recognize circular traffic signs with high accuracy and robust... Recognizing various traffic signs,especially the popular circular traffic signs,is an essential task for implementing advanced driver assistance system.To recognize circular traffic signs with high accuracy and robustness,a novel approach which uses the so-called improved constrained binary fast radial symmetry(ICBFRS) detector and pseudo-zernike moments based support vector machine(PZM-SVM) classifier is proposed.In the detection stage,the scene image containing the traffic signs will be converted into Lab color space for color segmentation.Then the ICBFRS detector can efficiently capture the position and scale of sign candidates within the scene by detecting the centers of circles.In the classification stage,once the candidates are cropped out of the image,pseudo-zernike moments are adopted to represent the features of extracted pictogram,which are then fed into a support vector machine to classify different traffic signs.Experimental results under different lighting conditions indicate that the proposed method has robust detection effect and high classification accuracy. 展开更多
关键词 traffic sign recognition circle detection fast radial symmetry detector pseudo-zernike moments support vector machine
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Logistic Regression Based Model for Improving the Accuracy and Time Complexity of ROI’s Extraction in Real Time Traffic Signs Recognition System
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作者 Fareed Qararyah Yousef-Awwad Daraghmi Eman Yasser Daraghmi 《Journal of Computer Science Research》 2019年第1期10-15,共6页
Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system.Traffic sign re... Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system.Traffic sign recognition systems consist of an initial detection phase where images transportaand colors are segmented and fed to the recognition phase.The most challenging process in such systems in terms of time consumption is the detection phase.The trade off in previous studies,which proposed different methods for detecting traffic signs,is between accuracy and computation time,Therefore,this paper presents a novel accurate and time-efficient color segmentation approach based on logistic regression.We used RGB color space as the domain to extract the features of our hypothesis;this has boosted the speed of our approach since no color conversion is needed.Our trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting conditions.The results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method. 展开更多
关键词 Traffic sign recognition systems Logistic regression
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Automatic Mexican Sign Language Recognition Using Normalized Moments and Artificial Neural Networks 被引量:1
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作者 Francisco Solís David Martínez Oscar Espinoza 《Engineering(科研)》 2016年第10期733-740,共8页
This document presents a computer vision system for the automatic recognition of Mexican Sign Language (MSL), based on normalized moments as invariant (to translation and scale transforms) descriptors, using artificia... This document presents a computer vision system for the automatic recognition of Mexican Sign Language (MSL), based on normalized moments as invariant (to translation and scale transforms) descriptors, using artificial neural networks as pattern recognition model. An experimental feature selection was performed to reduce computational costs due to this work focusing on automatic recognition. The computer vision system includes four LED-reflectors of 700 lumens each in order to improve image acquisition quality;this illumination system allows reducing shadows in each sign of the MSL. MSL contains 27 signs in total but 6 of them are expressed with movement;this paper presents a framework for the automatic recognition of 21 static signs of MSL. The proposed system achieved 93% of recognition rate. 展开更多
关键词 Mexican sign Language Automatic sign Language recognition Normalized Moments Computer Vision System
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Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model
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作者 Badriyya B.Al-onazi Mohamed K.Nour +6 位作者 Hussain Alshahran Mohamed Ahmed Elfaki Mrim M.Alnfiai Radwa Marzouk Mahmoud Othman Mahir M.Sharif Abdelwahed Motwakel 《Computers, Materials & Continua》 SCIE EI 2023年第5期3413-3429,共17页
Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enha... Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enhanced outcomes.But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks.This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)model.The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures.The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)model.For gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language gestures.Lastly,the DHO algorithm is utilized for parameter optimization of the MLP model.The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects.The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%. 展开更多
关键词 Machine learning sign language recognition multilayer perceptron deer hunting optimization densenet
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Attitudes Towards the Official Recognition of Hong Kong Sign Language by Hong Kong Citizens
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作者 Linghui Gan Federico Gobbo 《Journal of Linguistics and Education Research》 2019年第2期28-43,共16页
This paper is a pilot study that investigates the attitudes towards the official recognition of Hong Kong Sign Language(HKSL)by Hong Kong citizens.We used video-chat software(mainly WhatsApp,and Facebook Messenger,but... This paper is a pilot study that investigates the attitudes towards the official recognition of Hong Kong Sign Language(HKSL)by Hong Kong citizens.We used video-chat software(mainly WhatsApp,and Facebook Messenger,but also FaceTime)to conduct long-distance semi-structured interviews with 30 participants grouped as deaf,hearing-related(hearing people that are closely involved in the Deaf community),and hearing-unrelated(hearing people that have little contact with deaf people and the Deaf community).Results show that the majority of participants(N=22)holds a supportive attitude towards the recognition of HKSL;Five participants hold a neutral position,and three participants hold a negative attitude towards it.We discussed each type of attitude in detail.Results show that participants’attitudes are positively related to their awareness of deaf people’s need,the understanding of‘language recognition’,and personal world views.In other words,the more participants are aware,the more they foster official recognition,at least as a general trend.Results also indicate that hearing people who are not involved in the Deaf community know very little about deaf people and the Deaf community,in general.At the end of the paper,we also reflect on two issues:we argue that the standardization of HKSL plays an important role in deaf education and empowering citizenship awareness and participation. 展开更多
关键词 sign language recognition Official language Status planning Language attitude HKSL
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An Efficient Framework for Indian Sign Language Recognition Using Wavelet Transform
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作者 Mathavan Suresh Anand Nagarajan Mohan Kumar Angappan Kumaresan 《Circuits and Systems》 2016年第8期1874-1883,共10页
Hand gesture recognition system is considered as a way for more intuitive and proficient human computer interaction tool. The range of applications includes virtual prototyping, sign language analysis and medical trai... Hand gesture recognition system is considered as a way for more intuitive and proficient human computer interaction tool. The range of applications includes virtual prototyping, sign language analysis and medical training. In this paper, an efficient Indian Sign Language Recognition System (ISLR) is proposed for deaf and dump people using hand gesture images. The proposed ISLR system is considered as a pattern recognition technique that has two important modules: feature extraction and classification. The joint use of Discrete Wavelet Transform (DWT) based feature extraction and nearest neighbour classifier is used to recognize the sign language. The experimental results show that the proposed hand gesture recognition system achieves maximum 99.23% classification accuracy while using cosine distance classifier. 展开更多
关键词 Hand Gesture sign Language recognition THRESHOLDING Wavelet Transform Nearest Neighbour Classifier
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Wearable multifunctional organohydrogel-based electronic skin for sign language recognition under complex environments
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作者 Bin Song Xudong Dai +1 位作者 Xin Fan Haibin Gu 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2024年第14期91-103,共13页
Language barrier is the main cause of disagreement.Sign language,which is a common language in all the worldwide language families,is difficult to be entirely popularized due to the high cost of learning as well as th... Language barrier is the main cause of disagreement.Sign language,which is a common language in all the worldwide language families,is difficult to be entirely popularized due to the high cost of learning as well as the technical barrier in real-time translation.To solve these problems,here,we constructed a wearable organohydrogel-based electronic skin(e-skin)with fast self-healing,strong adhesion,extraor-dinary anti-freezing and moisturizing properties for sign language recognition under complex environ-ments.The e-skin was obtained by using an acrylic network as the main body,aluminum(III)and bay-berry tannin as the crosslinking agent,water/ethylene glycol as the solvent system,and a polyvinyl al-cohol network to optimize the network performance.Using this e-skin,a smart glove was further built,which could carry out the large-scale data collection of common gestures and sign languages.With the help of the deep learning method,specific recognition and translation for various gestures and sign lan-guages could be achieved.The accuracy was 93.5%,showing the ultra-high classification accuracy of a sign language interpreter.In short,by integrating multiple characteristics and combining deep learning technology with hydrogel materials,the e-skin achieved an important breakthrough in human-computer interaction and artificial intelligence,and provided a feasible strategy for solving the dilemma of mutual exclusion between flexible electronic devices and human bodies. 展开更多
关键词 sign language recognition Conductive organohydrogel Electronic skin Strain sensor SELF-HEALING Freezing resistance
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