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Position and Rotation Invariant Sign Language Recognition from 3D Point Cloud Data with Recurrent Neural Networks [article]

Prasun Roy and Saumik Bhattacharya and Partha Pratim Roy and Umapada Pal
2020 arXiv   pre-print
A recurrent neural network (RNN) is employed as classifier. To improve performance of the classifier, we use geometric transformation for alignment correction of depth frames.  ...  Sign language is a gesture based symbolic communication medium among speech and hearing impaired people.  ...  Conclusion In this paper, we have presented a position and rotation invariant, user independent sign language recognition system using LSTM network.  ... 
arXiv:2010.12669v1 fatcat:aebjykqgcbaffpezina44ax57q

A survey on deep geometry learning: From a representation perspective

Yun-Peng Xiao, Yu-Kun Lai, Fang-Lue Zhang, Chunpeng Li, Lin Gao
2020 Computational Visual Media  
Unlike 2D images, which can be uniformly represented by a regular grid of pixels, 3D shapes have various representations, such as depth images, multi-view images, voxels, point clouds, meshes, implicit  ...  Researchers have achieved great success in dealing with 2D images using deep learning. In recent years, 3D computer vision and geometry deep learning have gained ever more attention.  ...  The structure of 3D shapes is processed by recurrent neural networks (RNNs) [11] , recursive neural networks (RvNNs) [12] , or other network architectures.  ... 
doi:10.1007/s41095-020-0174-8 fatcat:kpoynaixq5esbek63bovybisfa

A Survey on Deep Geometry Learning: From a Representation Perspective [article]

Yun-Peng Xiao, Yu-Kun Lai, Fang-Lue Zhang, Chunpeng Li, Lin Gao
2020 arXiv   pre-print
Researchers have now achieved great success on dealing with 2D images using deep learning. In recent years, 3D computer vision and Geometry Deep Learning gain more and more attention.  ...  Therefore, in this survey, we review recent development in deep learning for 3D geometry from a representation perspective, summarizing the advantages and disadvantages of different representations in  ...  The structure of 3D shapes is processed by Recurrent Neural Networks (RNNs) [121] , Recursive Neural Networks (RvNNs) [51] or other network architectures.  ... 
arXiv:2002.07995v2 fatcat:pustwlu5freypnccfrculkqvei

A Deep Learning-based Multimodal Depth-Aware Dynamic Hand Gesture Recognition System [article]

Hasan Mahmud, Mashrur M. Morshed, Md. Kamrul Hasan
2021 arXiv   pre-print
Previously, researchers have explored depth and 2D-skeleton-based multimodal fusion CRNNs (Convolutional Recurrent Neural Networks) but have had limitations in getting expected recognition results.  ...  The dynamic hand gesture recognition task has seen studies on various unimodal and multimodal methods.  ...  We hope that our work fuels further research in the field of multimodal dynamic hand gesture recognition.  ... 
arXiv:2107.02543v2 fatcat:uqaqdyypwrervagh2oet7hragm

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

Munir Oudah, Ali Al-Naji, Javaan Chahl
2020 Journal of Imaging  
In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification  ...  In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two.  ...  Acknowledgments: The authors would like to thank the staff in Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq and the participants for their support to conduct the  ... 
doi:10.3390/jimaging6080073 pmid:34460688 fatcat:zmid23k67vbozb54sfji4nlfiy

Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM

Nhu-Tai Do, Soo-Hyung Kim, Hyung-Jeong Yang, Guee-Sang Lee
2020 Applied Sciences  
For the hand skeleton shape approach, we use the movement, the rotations of the hand joints with respect to their neighbors, and the skeletal point-cloud to learn the 3D geometric transformation.  ...  This study builds robust hand shape features from the two modalities of depth and skeletal data for the dynamic hand gesture recognition problem.  ...  [32] performed training on a bidirectional Recurrent Neural Network (RNN) using the movement features of fingers and hand and skeleton sequences.  ... 
doi:10.3390/app10186293 fatcat:b6mblxdf7rdy5oewsraf36wzdq

Handshape Recognition Using Skeletal Data

Tomasz Kapuscinski, Patryk Organisciak
2018 Sensors  
It encodes the relative differences between vectors associated with the pointing directions of the particular fingers and the palm normal.  ...  In this paper, a method of handshapes recognition based on skeletal data is described. A new feature vector is proposed.  ...  The SVM classifier was used to differentiate between manual and finger spelling sequences and the Bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks were used for manual sign and fingerspelled  ... 
doi:10.3390/s18082577 pmid:30082649 fatcat:74tqz5st4bb6lpbnt2glot2pte

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks [article]

Baichuan Huang, Jun Zhao, Jingbin Liu
2020 arXiv   pre-print
The open question and forward thinking with an envision in 6G wireless networks end the paper.  ...  Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception.  ...  Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud.  ... 
arXiv:1909.05214v4 fatcat:itnluvkewfd6fel7x65wdgig3e

Visual Methods for Sign Language Recognition: A Modality-Based Review [article]

Bassem Seddik, Najoua Essoukri Ben Amara
2020 arXiv   pre-print
Sign language visual recognition from continuous multi-modal streams is still one of the most challenging fields.  ...  This paper aims at reviewing the human actions recognition literature with the sign-language visual understanding as a scope.  ...  A number of methods have also changed the data format from depth images to sets of 3D point clouds or 3D meshes after triangulation [154] .  ... 
arXiv:2009.10370v1 fatcat:jkqtzid6qndhnijs5axhfom4ia

User-Independent American Sign Language Alphabet Recognition Based on Depth Image and PCANet Features

Walaa Aly, Saleh Alya, Sultan Almotairi
2019 IEEE Access  
Existing color-based sign language recognition systems suffer from many challenges such as complex background, hand segmentation, large inter-class and intra-class variations.  ...  Sign language is the most natural and effective way for communications among deaf and normal people.  ...  The 3D local support surface was characterized by the 3D Facet associated with the 3D cloud point. The 3D shapes and structures of various signs are represented by the H3DF descriptor.  ... 
doi:10.1109/access.2019.2938829 fatcat:ogza6lq56vgznpk25n6tbdg6pe

Hand Pose Recognition Using Parallel Multi Stream CNN

Iram Noreen, Muhammad Hamid, Uzma Akram, Saadia Malik, Muhammad Saleem
2021 Sensors  
Recently, several computer applications provided operating mode through pointing fingers, waving hands, and with body movement instead of a mouse, keyboard, audio, or touch input such as sign language  ...  Support vector machines and neural networks have been extensively used in this domain using conventional RGB data, which are not very effective for adequate performance.  ...  Data Availability Statement: Data sharing is not applicable to this article. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21248469 pmid:34960562 pmcid:PMC8708730 fatcat:6zjdtwffufccvoyq77yl3ln3vq

A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications

Abubakar Sulaiman Gezawa, Yan Zhang, Qicong Wang, Lei Yunqi
2020 IEEE Access  
According to the findings in this work, multi views methods surpass voxel-based methods and with increased layers and enough data augmentation the performance can still be increased.  ...  Therefore, it can be concluded that deep learning together with a suitable 3D data representation gives an effective approach for improving the performance of 3D shape analysis.  ...  informative shape descriptor using adversarial neural networks that train a combination of convolutional neural network, adversarial discriminative and recurrent neural network. 3D shape features that  ... 
doi:10.1109/access.2020.2982196 fatcat:jnya5rscynf3zm7efuucqxafri

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 4445-4460 Hierarchical Recurrent Deep Fusion Using Adaptive Clip Summarization for Sign Language Translation.  ...  ., +, TIP 2020 4772-4787 Data compression 3D Point Cloud Attribute Compression Using Geometry-Guided Sparse Representation.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

A survey on Deep Learning Advances on Different 3D Data Representations [article]

Eman Ahmed, Alexandre Saint, Abd El Rahman Shabayek, Kseniya Cherenkova, Rig Das, Gleb Gusev, Djamila Aouada, Bjorn Ottersten
2019 arXiv   pre-print
Recently, with the availability of both large 3D datasets and computational power, it is today possible to consider applying deep learning to learn specific tasks on 3D data such as segmentation, recognition  ...  3D data is a valuable asset the computer vision filed as it provides rich information about the full geometry of sensed objects and scenes.  ...  SO-Net is a permutation invariant network that can tolerate unordered point clouds inputs.  ... 
arXiv:1808.01462v2 fatcat:iuoay2sddjdqjbgm2nai6pa7gq

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Fahad Jibrin Abdu, Yixiong Zhang, Maozhong Fu, Yuhan Li, Zhenmiao Deng
2021 Sensors  
Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used.  ...  The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective  ...  tensors (d) Sample of Radar point clouds (red) with 3D annotations (green) and Lidar point clouds (grey) from the Nuscenes dataset.  ... 
doi:10.3390/s21061951 pmid:33802217 pmcid:PMC7999239 fatcat:4sek2e2parf2vpfatqhe7m5sjy
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