115 research outputs found
Application of Cascade Hand Detection for Touchless Interaction in Virtual Design
This paper proposes a method for robust hand detection for interactive touchless display using cascade classifier technique. A hardware system comprising a transparent display, two video cameras and a projector is assembled to generate on-display-surface object images for touchless display. A well-trained cascade of boosted classifiers is applied to detect the position of the hand in the object image. Using this method, accurate and robust hand detection for touchless display can be achieved. The detected hand trajectory information can be converted into mouse and keyboard inputs for interactive control and manipulation in virtual applications like virtual assembly and virtual design
A new framework for sign language recognition based on 3D handshape identification and linguistic modeling
Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory
conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions
and off-plane rotations), and/or achieve limited success. Here we propose a new framework that (1) provides a new tracking method
less dependent than others on laboratory conditions and able to deal with variations in background and skin regions (such as the
face, forearms, or other hands); (2) allows for identification of 3D hand configurations that are linguistically important in American
Sign Language (ASL); and (3) incorporates statistical information reflecting linguistic constraints in sign production. For purposes of
large-scale computer-based sign language recognition from video, the ability to distinguish hand configurations accurately is critical.
Our current method estimates the 3D hand configuration to distinguish among 77 hand configurations linguistically relevant for
ASL. Constraining the problem in this way makes recognition of 3D hand configuration more tractable and provides the information
specifically needed for sign recognition. Further improvements are obtained by incorporation of statistical information about linguistic
dependencies among handshapes within a sign derived from an annotated corpus of almost 10,000 sign tokens
A comparative study of different image features for hand gesture machine learning
Vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition. Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. In this paper we present a comparative study of seven different algorithms for hand feature extraction, for static hand gesture classification, analysed with RapidMiner in order to find the best learner.
We defined our own gesture vocabulary, with 10 gestures, and we have recorded videos from 20 persons performing the gestures for later processing. Our goal in the present study is to learn features that, isolated, respond better in various situations in human-computer interaction. Results show that the radial signature and the centroid distance are the features that when used separately obtain better results, being at the same time simple in terms of computational complexity.(undefined
CNN Based Posture-Free Hand Detection
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 and hand poses. However the CNN approach is complex and can
increase computational time, which at the end reduce its effectiveness on a
system where the speed is essential.In this study we propose a shallow CNN
network which is fast, and insensitive to translation and hand poses. It is
tested on two different domains of hand datasets, and performs in relatively
comparable performance and faster than the other state-of-the-art hand
CNN-based hand detection method. Our evaluation shows that the proposed shallow
CNN network performs at 93.9% accuracy and reaches much faster speed than its
competitors.Comment: 4 pages, 5 figures, in The 10th International Conference on
Information Technology and Electrical Engineering 2018, ISBN:
978-1-5386-4739-
Hand gesture recognition for human computer interaction: a comparative study of different image features
Hand gesture recognition for human computer interaction, being a
natural way of human computer interaction, is an area of active research in
computer vision and machine learning. This is an area with many different possible
applications, giving users a simpler and more natural way to communicate
with robots/systems interfaces, without the need for extra devices. So, the primary
goal of gesture recognition research is to create systems, which can identify
specific human gestures and use them to convey information or for device
control. For that, vision-based hand gesture interfaces require fast and extremely
robust hand detection, and gesture recognition in real time. In this study
we try to identify hand features that, isolated, respond better in various situations
in human-computer interaction. The extracted features are used to train a
set of classifiers with the help of RapidMiner in order to find the best learner. A
dataset with our own gesture vocabulary consisted of 10 gestures, recorded
from 20 users was created for later processing. Experimental results show that
the radial signature and the centroid distance are the features that when used
separately obtain better results, with an accuracy of 91% and 90,1% respectively
obtained with a Neural Network classifier. These to methods have also
the advantage of being simple in terms of computational complexity, which
make them good candidates for real-time hand gesture recognition
Real-time, long-term hand tracking with unsupervised initialization
This paper proposes a complete tracking system that is capable of long-term, real-time hand tracking with unsupervised initialization and error recovery. Initialization is steered by a three-stage hand detector, combining spatial and temporal information. Hand hypotheses are generated by a random forest detector in the first stage, whereas a simple linear classifier eliminates false positive detections. Resulting detections are tracked by particle filters that gather temporal statistics in order to make a final decision. The detector is scale and rotation invariant, and can detect hands in any pose in unconstrained environments. The resulting discriminative confidence map is combined with a generative particle filter based observation model to enable robust, long-term hand tracking in real-time. The proposed solution is evaluated using several challenging, publicly available datasets, and is shown to clearly outperform other state of the art object tracking methods
Vision-based portuguese sign language recognition system
Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system
Contextual Attention for Hand Detection in the Wild
We present Hand-CNN, a novel convolutional network architecture for detecting hand masks and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN with a novel attention mechanism to incorporate contextual cues in the detection process. This attention mechanism can be implemented as an efficient network module that captures non-local dependencies between features. This network module can be inserted at different stages of an object detection network, and the entire detector can be trained end-to-end. We also introduce large-scale annotated hand datasets containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on the newly collected datasets and the publicly available PASCAL VOC human layout dataset. Data and code: https://www3.cs.stonybrook.edu/~cvl/projects/hand_det_attention
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