Virtual Keyboards With Real-Time and Robust Deep Learning-Based Gesture Recognition

Lee, Tae-Ho; Kim, Sunwoong; Kim, Taehyun; Kim, Jin-Sung; Lee, Hyuk-Jae

doi:10.1109/thms.2022.3165165

Cited by 5 publications

(4 citation statements)

References 34 publications

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“…Head-pointing cursor and pinch (Frutos-Pascual et al, 2019;Serrano et al, 2022); 8. Virtual continuous keyboard (Lee et al, 2019;Lee et al, 2022); 9. Uncommon dynamic gestures for continuous outputs (Sun et al, 2019); 10.…”

Section: Interactions Mainly Gesturalmentioning

confidence: 99%

“…The gesture is totally different from the writing or the typing activity in the physical world. Similarly, Lee et al (2022) use a specific gesture that uses the number of extended fingers as well as a thumb motion to trigger the typing. In the group 9, the only article is the work from Sun et al (2019) and it describes a dynamic gesture which is triggered by a finger extension based pose and then map movement of the hand to a continuous value on a smart connected object.…”

Section: Interactions Mainly Gesturalmentioning

confidence: 99%

“…• track the hand joints (Mueller et al, 2017;Zhang et al, 2020); • detect gestures (Hu et al, 2018;Sun et al, 2019;Zhang et al, 2020;Caputo et al, 2021;Su et al, 2021;Lee et al, 2022); • process trajectories (Lu et al, 2019;Zhang et al, 2020).…”

Section: Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Hand interaction designs in mixed and augmented reality head mounted display: a scoping review and classification

Nguyen,

Gouin-Vallerand,

Amiri

2023

Front. Virtual Real.

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Mixed reality has made its first step towards democratization in 2017 with the launch of a first generation of commercial devices. As a new medium, one of the challenges is to develop interactions using its endowed spatial awareness and body tracking. More specifically, at the crossroad between artificial intelligence and human-computer interaction, the goal is to go beyond the Window, Icon, Menu, Pointer (WIMP) paradigm humans are mainly using on desktop computer. Hand interactions either as a standalone modality or as a component of a multimodal modality are one of the most popular and supported techniques across mixed reality prototypes and commercial devices. In this context, this paper presents scoping literature review of hand interactions in mixed reality. The goal of this review is to identify the recent findings on hand interactions about their design and the place of artificial intelligence in their development and behavior. This review resulted in the highlight of the main interaction techniques and their technical requirements between 2017 and 2022 as well as the design of the Metaphor-behavior taxonomy to classify those interactions.

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Section: Interactions Mainly Gesturalmentioning

confidence: 99%

Section: Interactions Mainly Gesturalmentioning

confidence: 99%

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Hand interaction designs in mixed and augmented reality head mounted display: a scoping review and classification

Nguyen,

Gouin-Vallerand,

Amiri

2023

Front. Virtual Real.

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“…According to the characteristics of X-ray images and the ability of convolutional neural network [13][14][15][16][17][18], the edges and contours of objects in X-ray images play a crucial role in detecting the prohibited items we want to find. In addition, we need to realize that the regions and the semantic information in neural networks are also quite useful at the same time.…”

Section: Introductionmentioning

confidence: 99%

Combination of deep learning with representation learning in X-ray prohibited item detection

et al. 2023

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During X-ray inspection detection, a detector converts the collected X-rays from objects into electrical signals, which are then transmitted to a computer for image processing and analysis. From the aspect of digital image processing, detection tasks mainly focus on data processing and transformation to identify valuable features, which make the algorithms more effective. The consistent requirement for speed and accuracy in X-ray prohibited item detection is still not fully satisfied, especially in pictures obtained under special imaging conditions. For noisy X-ray images with heavy occlusion, a direct and suitable approach of representation learning is the optimal solution. According to our study, we realized that heterogeneous information fusion from different extraction approaches can be applied effectively to overcome this issue. We proposed two innovative algorithms to extract effective features of X-ray objects to significantly improve the efficiency of X-ray prohibited item detection. The brief model we proposed fuses the representations learned from the noisy X-ray images and outperforms the best model (DOAM-O) so far on OPIXray. Furthermore, the attention module we designed to select information on deep learning and representation strengthens the model; considering this, the model utilizes lesser time for both training and inference, which makes it easier to be trained on a lightweight computing device.

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Effective Gesture-Based Framework for Capturing User Input

Sri¹,

Gupta²,

Agrawal³

et al. 2023

Advances in Intelligent Systems and Computing

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Virtual Keyboards With Real-Time and Robust Deep Learning-Based Gesture Recognition

Cited by 5 publications

References 34 publications

Hand interaction designs in mixed and augmented reality head mounted display: a scoping review and classification

Hand interaction designs in mixed and augmented reality head mounted display: a scoping review and classification

Combination of deep learning with representation learning in X-ray prohibited item detection

Effective Gesture-Based Framework for Capturing User Input

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