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Virtual reality has proved to be useful in applications in several fields ranging from gaming, medicine, and training to development of interfaces that enable human-robot collaboration. It empowers designers to explore applications outside…
Deep learning expresses a category of machine learning algorithms that have the capability to combine raw inputs into intermediate features layers. These deep learning algorithms have demonstrated great results in different fields. Deep…
Hand gesture recognition is a strenuous task to solve in videos. In this paper, we use a 3D residual attention network which is trained end to end for hand gesture recognition. Based on the stacked multiple attention blocks, we build a 3D…
Recently, deep learning has been successfully applied to robotic grasp detection. Based on convolutional neural networks (CNNs), there have been lots of end-to-end detection approaches. But end-to-end approaches have strict requirements for…
Classifying the behavior of humans or animals from videos is important in biomedical fields for understanding brain function and response to stimuli. Action recognition, classifying activities performed by one or more subjects in a trimmed…
Hand gestures are an intuitive, socially acceptable, and a non-intrusive interaction modality in Mixed Reality (MR) and smartphone based applications. Unlike speech interfaces, they tend to perform well even in shared and public spaces.…
Existing RGB-based 2D hand pose estimation methods learn the joint locations from a single resolution, which is not suitable for different hand sizes. To tackle this problem, we propose a new deep learning-based framework that consists of…
Human pose analysis has garnered significant attention within both the research community and practical applications, owing to its expanding array of uses, including gaming, video surveillance, sports performance analysis, and…
In this paper, we have proposed a system based on K-L Transform to recognize different hand gestures. The system consists of five steps: skin filtering, palm cropping, edge detection, feature extraction, and classification. Firstly the hand…
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI). These…
Ultrasound imaging of the forearm has demonstrated significant potential for accurate hand gesture classification. Despite this progress, there has been limited focus on developing a stand-alone end- to-end gesture recognition system which…
Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology. However, dexterous grasping is much more under-explored than object grasping with parallel grippers,…
Although many studies suggest high performance hand detection methods, those methods are likely to be overfitting. Fortunately, the Convolution Neural Network (CNN) based approach provides a better way that is less sensitive to translation…
Hand washing is a crucial part of personal hygiene. Hand washing detection is a relevant topic for wearable sensing with applications in the medical and professional fields. Hand washing detection can be used to aid workers in complying…
In recent years robots have become an important part of our day-to-day lives with various applications. Human-robot interaction creates a positive impact in the field of robotics to interact and communicate with the robots. Gesture…
Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition…
Robust and human-like dexterous grasping of general objects is a critical capability for advancing intelligent robotic manipulation in real-world scenarios. However, existing reinforcement learning methods guided by grasp priors often…
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that…
With the ongoing increase in the worldwide population and escalating consumption habits,there's a surge in the amount of waste produced.The situation poses considerable challenges for waste management and the optimization of recycling…
We develop a system for modeling hand-object interactions in 3D from RGB images that show a hand which is holding a novel object from a known category. We design a Convolutional Neural Network (CNN) for Hand-held Object Pose and Shape…