Hand number gesture recognition using recognized hand parts in depth images

Dinh, Dong-Luong; Lee, Sungyoung; Kim, Tae‐Seong

doi:10.1007/s11042-014-2370-y

Cited by 20 publications

(7 citation statements)

References 18 publications

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“…Information technology plays an important role in the transformation and upgrading of the library. e use of Internet of ings, cloud computing, wearable technology, virtual reality technology, artificial intelligence, and other technologies can realize the optimization of the library's literature, equipment, personnel, and buildings [18,19]. Based on the problems in the application of the Internet of ings identification technology in the smart library, the improvement measures for the construction of the smart library are proposed [20,21].…”

Section: Introductionmentioning

confidence: 99%

Visualization of Information Retrieval in Smart Library Based on Virtual Reality Technology

Fang

2020

Complexity

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Starting from the virtual reality technology, the characteristics of its most suitable combination with the library are explored, so as to lay a foundation in theory and practice to promote the development of virtual reality in the library. In the concentration camp of the latest advanced technology, the relevant technologies used in the various levels of models in the smart library are extracted, and their functional principles and applications are systematically introduced; Chapter 4 builds the level of the smart library information retrieval technology model. The structure diagram, various levels of functions, and related smart service models are discussed. Research is on context construction of extended resources of knowledge service in smart libraries. The elements and content of the resource context are introduced, and the strategy of constructing the resource context for the extension of the knowledge service of the smart library is proposed. Research is on the construction of the context of the extension and interconnection of knowledge services in smart libraries. The relevant elements and contents of the technical context and the spatial context are introduced, and a strategy for constructing a connected context for the extension of the knowledge service of a smart library is proposed. Research is on the context construction of knowledge service extension of smart library. The elements and contents of the service context are introduced, and the strategy of constructing the service context of the knowledge service extension of the smart library is proposed. A visual model of information retrieval is constructed. The model integrates the core steps of the information visualization process and introduces the information space and functional system (navigation, organization, indexing, and retrieval) in the information construction theory into it and through five mapping layers (functional space mapping, visualization space mapping, visualization mapping, view mapping, and interactive mapping) and six spatial layers (role space, information space, functional space, visualized information space, visualized object space, and visualized view space), which describe visualization applications that target the user experience model in information construction build process.

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Section: Introductionmentioning

confidence: 99%

Visualization of Information Retrieval in Smart Library Based on Virtual Reality Technology

Fang

2020

Complexity

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“…The experiment demonstrated accuracy for the same subject is 99.56% and accuracy in condition of low light is 97.26%. There are other researches which use different design methods to recognize CSL [138][139][140][141][142][143][144]. For instance, Zhang et al [103] applied HOD to draw trajectories and employed multi-SVM to classify for isolated Sign Language Recognition.…”

Section: Investigation Of Chinese Sign Language Recognitionmentioning

confidence: 99%

A Survey on Artificial Intelligence in Chinese Sign Language Recognition

et al. 2020

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Chinese Sign Language (CSL) offers the main means of communication for the hearing impaired in China. Sign Language Recognition (SLR) can shorten the distance between the hearing-impaired and healthy people and help them integrate into the society. Therefore, SLR has become the focus of sign language application research. Over the years, the continuous development of new technologies provides a source and motivation for SLR. This paper aims to cover the most recent approaches in Chinese Sign Language Recognition (CSLR). With a thorough review of superior methods from 2000 to 2019 in CSLR researches, various techniques and algorithms such as scale-invariant feature transform, histogram of oriented gradients, wavelet entropy, Hu moment invariant, Fourier descriptor, gray-level co-occurrence matrix, dynamic time warping, principal component analysis, autoencoder, hidden Markov model (HMM), support vector machine (SVM), random forest, skin color modeling method, k-NN, artificial neural network, convolutional neural network (CNN), and transfer learning are discussed in detail, which are based on several major stages, that is, data acquisition, preprocessing, feature extraction, and classification. CSLR was summarized from some aspect as follows: methods of classification and feature extraction, accuracy/performance evaluation, and sample size/datasets. The advantages and limitations of different CSLR approaches were compared. It was found that data acquisition is mainly through Kinect and camera, and the feature extraction focuses on hand's shape and spatiotemporal factors, but ignoring facial expressions. HMM and SVM are used most in the classification. CNN is becoming more and more popular, and a deep neural network-based recognition approach will be the future trend. However, due to the complexity of the contemporary Chinese language, CSLR generally has a lower accuracy than other SLR. It is necessary to establish an appropriate dataset to conduct comparable experiments. The issue of decreasing accuracy as the dataset increases needs to resolve. Overall, our study is hoped to give a comprehensive presentation for those people who are interested in CSLR and SLR and to further contribute to the future research.

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“…The recognition rate attained is 99.9% on live depth images in real-time which is good enough for a system of this nature. [4] designed a number gesture recognition system based on the recognized hand parts in depth images taken by the Kinect. They proposed an approach that consisted of two main stages, hand parts recognition by random forests (RFs) and rule-based hand number gesture recognition.…”

Section: Related Studiesmentioning

confidence: 99%

Recognition of static hand gesture with using ANN and SVM

Bamwenda¹,

Özerdem²

2019

DÜMF Mühendislik Dergisi

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Hand gesture recognition is a relevant study topic for a reason that sometimes we may not be in position to communicate verbally. There is need to design Hand gesture recognition systems in order to help people adopt to nonverbal communication mainly sign language. However, there is no clue to understand the meaning of gesture through the computers directly. So this calls for definitions that generalize models in a computer. That is why the machine-learning approaches are implemented in recognition systems. There are generally two types of hand gestures recognition systems which researches have concentrated on. These include static and dynamic Hand gesture recognition systems. However, in building Hand gesture recognition systems, various machine learning approaches have been used. For implementing the proposed system, MS Kinect depth sensor was used as a hardware. The Kinect depth sensor is composed of an infrared camera. This is an advantage to the systems that are designed basing on the depth sensing because factors like color, clothing and background have less effect on the performance. So Kinect based depth sensor systems have a high accuracy and performance making them relevant and applicable in our daily lives. In this paper, we propose a static hand gesture recognition system in real time using two machine learning methods namely Support Vector Machine and Artificial Neural Networks. We use of the newly launched Microsoft Kinect sensor for image extraction. The sensor helps us to extract the hand images. We implement the system on a Matlab platform for reasons that Matlab is widely used by researchers in different fields and that can handle complex computations. In the training of the model, we collect a hundred depth-based Histogram of Oriented Gradient features per alphabet from the hand gesture images which we trained, tested and validated using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). From this dataset, we can generate the generalized gesture model for each alphabet image. For the proposed system, the classification with ANN proves a higher performance then SVM.

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Hand number gesture recognition using recognized hand parts in depth images

Cited by 20 publications

References 18 publications

Visualization of Information Retrieval in Smart Library Based on Virtual Reality Technology

Visualization of Information Retrieval in Smart Library Based on Virtual Reality Technology

A Survey on Artificial Intelligence in Chinese Sign Language Recognition

Recognition of static hand gesture with using ANN and SVM

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