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Gradient local auto-correlation features for depth human action recognition
2021
SN Applied Sciences
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In the approach, the enhanced motion and static history images are computed and a set of 2D auto-correlation gradient feature vectors is obtained from them to describe an action. ...
Kernel-based Extreme Learning Machine is used with the extracted features to distinguish the diverse action types promisingly. ...
In addition, we can obtain body shape and structure characteristics and the human skeleton information from depth images. ...
doi:10.1007/s42452-021-04528-1
fatcat:zpumt5f25fdydlowttzcqjrm4e
Stacked sparse autoencoder and history of binary motion image for human activity recognition
2018
Multimedia tools and applications
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In this paper, a supervised way followed by an unsupervised learning using the principle of the auto-encoder is proposed to address the problem. ...
Tools Appl (SSAE), an instance of a deep learning strategy, is presented for efficient human activities detection and the Softmax (SMC) for the classification. ...
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ...
doi:10.1007/s11042-018-6273-1
fatcat:jiltrfaj3nbubg2q2qm6ivjgtu
Structured Prediction of 3D Human Pose with Deep Neural Networks
[article]
2016
arXiv
pre-print
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In this paper, we introduce a Deep Learning regression architecture for structured prediction of 3D human pose from monocular images that relies on an overcomplete auto-encoder to learn a high-dimensional ...
They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a max-margin structured ...
Using auto-encoders for unsupervised feature learning has proven effective in several recognition tasks [16, 18, 35] . ...
arXiv:1605.05180v1
fatcat:mhpcvbkmsvefrfqn2q44gqdx3m
Deep Learning Methods for Cardiovascular Image
2019
Journal of Artificial Intelligence and Systems
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Cardiovascular disease is one of the most important diseases that endanger human health at present. It is very meaningful to diagnose and treat cardiovascular disease by means of in-depth learning. ...
learning, and then summarizes the application of deep learning in heart image segmentation, classification and other aspects combined with existing technologies. ...
Here, the authors need to declare whether or not the submitted work was carried out in the presence of any personal, professional or financial relationships that could potentially be construed as a conflict ...
doi:10.33969/ais.2019.11006
fatcat:egx5tibvm5dhriemz4dvx5ktsm
VR content creation and exploration with deep learning: A survey
2020
Computational Visual Media
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This article surveys recent research that uses such deep learning methods for VR content creation and exploration. ...
Intelligence of VR methods and applications has been significantly boosted by the recent developments in deep learning techniques. ...
Here, we review existing deep learning-based work for several major categories of reconstruction methods, namely for general scenes, human faces, and human bodies. ...
doi:10.1007/s41095-020-0162-z
fatcat:lgogzx26bvhn5f7uyefjkz7zny
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
., +, TMM 2021 4388-4399 Image denoising Adversarial 3D Convolutional Auto-Encoder for Abnormal Event Detection in Videos. ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
[article]
2020
arXiv
pre-print
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Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task ...
Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019. ...
a layer called sparse
non-homogeneous pooling layer to transform features
between bird's eye view and front view based on point cloud,
where the encoder backbone for RGB image and LiDAR data
is MSCNN ...
arXiv:2006.06091v3
fatcat:nhdgivmtrzcarp463xzqvnxlwq
Learning Gaze Transitions from Depth to Improve Video Saliency Estimation
[article]
2016
arXiv
pre-print
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In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing RGBD videos on regular 2D screens. ...
Our experiments indicate that integrating depth into video saliency calculation is beneficial. ...
[26] present a depth prior for saliency learned from human gaze information. ...
arXiv:1603.03669v1
fatcat:nezm5psydvf7zbf5ouph7kodee
Action recognition in videos
2012
2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)
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We briefly describe two recent contributions: joint level feature and sequence learning, as well as space-time graph matching. ...
Applications such as video surveillance, robotics, source selection, and video indexing often require the recognition of actions based on the motion of different actors in a video. ...
In this context, and as an extension of work in object recognition [9] , we proposed a translation variant sparse convolutional auto-encoder, which automatically learns a sparse feature extractor [1] ...
doi:10.1109/ipta.2012.6469480
dblp:conf/ipta/WolfB12
fatcat:627gostylvhtji5tvnsehvj4pq
Person identification from streaming surveillance video using mid-level features from joint action-pose distribution
2015
Video Surveillance and Transportation Imaging Applications 2015
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We propose a real time person identification algorithm for surveillance based scenarios from low-resolution streaming video, based on mid-level features extracted from the joint distribution of various ...
The proposed algorithm uses the combination of an auto-encoder based action association framework which produces per-frame probability estimates of the action being performed, and a pose recognition framework ...
Research in human action recognition can be grouped into different categories such as image models, sparse features and grammars/templates. 1 Some state of the art algorithms under the banner of image ...
doi:10.1117/12.2083423
fatcat:q3r7rqa5snffpanwvnaqcmbxzm
RGB-D-based Human Motion Recognition with Deep Learning: A Survey
[article]
2018
arXiv
pre-print
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In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. ...
The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. ...
Ijjina et al. [57] adopted stacked auto encoder to learn the underlying features of input skeleton data. ...
arXiv:1711.08362v2
fatcat:cugugpqeffcshnwwto4z2aw4ti
Human Pose Estimation from Monocular Images: A Comprehensive Survey
2016
Sensors
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The human pose detection problem has seen the most success when utilizing depth images in conjunction with color images: real-time estimation of 3D body joints and pixelwise body part labelling have been ...
Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. ...
In part-based methods, body part candidates are first detected from image evidence, and then detected body parts are assembled to fit image observations and a body plan [206] . ...
doi:10.3390/s16121966
pmid:27898003
pmcid:PMC5190962
fatcat:jigvz4ovpbh63eovto3etoefx4
Medical Image Fusion Method by Deep Learning
2021
International Journal of Cognitive Computing in Engineering
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Deep learning technology has been extensively explored in pattern recognition and image processing areas. ...
It cannot be only made up for the deficiencies of MRI, CT and SPECT image fusion, but also can be implemented in different types of multi-modal medical image fusion problems in batch processing mode, and ...
Acknowledgments The authors first sincerely thank the editors and anonymous reviewers for their constructive comments and suggestions, which are of great value to us. ...
doi:10.1016/j.ijcce.2020.12.004
fatcat:aeuelnzwlzdlld66lz334ivhwm
Rendering Portraitures from Monocular Camera and Beyond
[chapter]
2018
Lecture Notes in Computer Science
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In addition, we train a spatially-variant Recursive Neural Network to learn and accelerate this rendering process. ...
In this work, we introduce an automatic system that achieves portrait DoF rendering for monocular cameras. ...
Depth Estimation with Single Image. Deep learning based models have been used to learn depth from a single image, both in supervised and unsupervised ways. For supervised depth learning, Eigen et al. ...
doi:10.1007/978-3-030-01240-3_3
fatcat:wrjphnnkozdjbjdxoxqkpdcdni
Deep learning for detecting robotic grasps
2015
The international journal of robotics research
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In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents two main challenges. ...
Second, we need to handle multimodal inputs effectively, for which we present a method that applies structured regularization on the weights based on multimodal group regularization. ...
ACKNOWLEDGEMENTS We would like to thank Yun Jiang and Marcus Lim for useful discussions and help with baseline experiments. ...
doi:10.1177/0278364914549607
fatcat:vgo22gpforgb5mtyi4ogl27eny
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