Head-mounted gesture controlled interface for human-computer interaction

Memo, Alvise; Zanuttigh, Pietro

doi:10.1007/s11042-016-4223-3

Cited by 75 publications

(34 citation statements)

References 35 publications

(37 reference statements)

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“… Interaction with a display/projection (Bolt, 1980, Choi et al, 2007, Foehrenbach et al, 2009, Beyer and Meier, 2011, Asadzadeh et al, 2012, Cauchard et al, 2012, Xie and Xu, 2013, Rossol et al, 2014, Saxen et al, 2014, Adeen et al, 2015, Braun et al, 2017, Osti et al, 2017, Dondi et al, 2018, Ma et al, 2018.  Interaction with augmented reality (Reifinger et al, 2007, Lu et al, 2012, Bai et al, 2013, Hürst and van Wezel, 2013, Gangman and Yen, 2014, Adhikarla et al, 2015, Hernoux and Christmann, 2015, Shim et al, 2016, Saxen et al, 2014, Kim and Lee, 2016, Memo and Zanuttigh, 2018. Interaction with augmented reality included a variety of technologies that enable superimposed 3D representation of content and interaction with it.…”

Section: Manipulation/navigationmentioning

confidence: 99%

Systematic literature review of hand gestures used in human computer interaction interfaces

Vuletic

Duffy

Hay

et al. 2019

International Journal of Human-Computer Studies

125

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Section: Manipulation/navigationmentioning

confidence: 99%

Systematic literature review of hand gestures used in human computer interaction interfaces

Vuletic

Duffy

Hay

et al. 2019

International Journal of Human-Computer Studies

125

View full text Add to dashboard Cite

“…Datasets. We follow GestureGAN [10] and employ the NTU Hand Digit [48] and Creative Senz3D [49] datasets to evaluate the proposed AsymmetricGAN. The number of train/test image pair for the NTU Hand Digit and Creative Senz3D datasets are 75,036/9,600 and 135,504/12,800, respectively.…”

Section: B Hand Gesture-to-gesture Translation Taskmentioning

confidence: 99%

Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

Tang

Sebe

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

119

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State-of-the-art models for unpaired image-to-image translation with Generative Adversarial Networks (GANs) can learn the mapping from the source domain to the target domain using a cycle-consistency loss. The intuition behind these models is that if we translate from one domain to the other and back again we should arrive at where we started. However, existing methods always adopt a symmetric network architecture to learn both forward and backward cycles. Because of the task complexity and cycle input difference between the source and target image domains, the inequality in bidirectional forward-backward cycle translations is significant and the amount of information between two domains is different. In this paper, we analyze the limitation of the existing symmetric GAN models in asymmetric translation tasks, and propose an AsymmetricGAN model with both translation and reconstruction generators of unequal sizes and different parameter-sharing strategy to adapt to the asymmetric need in both unsupervised and supervised image-to-image translation tasks. Moreover, the training stage of existing methods has the common problem of model collapse that degrades the quality of the generated images, thus we explore different optimization losses for better training of AsymmetricGAN, and thus make image-to-image translation with higher consistency and better stability. Extensive experiments on both supervised and unsupervised generative tasks with several publicly available datasets demonstrate that the proposed AsymmetricGAN achieves superior model capacity and better generation performance compared with existing GAN models. To the best of our knowledge, we are the first to investigate the asymmetric GAN framework on both unsupervised and supervised image-to-image translation tasks. The source code, data and trained models are available at https://github.com/Ha0Tang/AsymmetricGAN.

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“…A detailed analysis of the various error sources has been presented in [19] while [20] focuses on the effects of the reflectivity of the scene on the depth accuracy. There exist large datasets acquired with ToF sensors for other computer vision applications like semantic segmentation [21,22], gesture recognition [23] and face recognition [24], but they all lack ground truth depth data that is very time consuming to acquire. For this reason the confidence of ToF data is typically computed with deterministic schemes.…”

Section: Related Workmentioning

confidence: 99%

Stereo and ToF data fusion by learning from synthetic data

et al. 2019

Self Cite

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Time-of-Flight (ToF) sensors and stereo vision systems are both capable of acquiring depth information but they have complementary characteristics and issues. A more accurate representation of the scene geometry can be obtained by fusing the two depth sources. In this paper we present a novel framework for data fusion where the contribution of the two depth sources is controlled by confidence measures that are jointly estimated using a Convolutional Neural Network. The two depth sources are fused enforcing the local consistency of depth data, taking into account the estimated confidence information. The deep network is trained using a synthetic dataset and we show how the classifier is able to generalize to different data, obtaining reliable estimations not only on synthetic data but also on real world scenes. Experimental results show that the proposed approach increases the accuracy of the depth estimation on both synthetic and real data and that it is able to outperform state-of-the-art methods.

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Head-mounted gesture controlled interface for human-computer interaction

Cited by 75 publications

References 35 publications

Systematic literature review of hand gestures used in human computer interaction interfaces

Systematic literature review of hand gestures used in human computer interaction interfaces

Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

Stereo and ToF data fusion by learning from synthetic data

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