Journal ArticleDOI
Attention Based Detection and Recognition of Hand Postures Against Complex Backgrounds
TLDR
A system for the detection, segmentation and recognition of multi-class hand postures against complex natural backgrounds using a Bayesian model of visual attention to generate a saliency map, and to detect and identify the hand region.Abstract:
A system for the detection, segmentation and recognition of multi-class hand postures against complex natural backgrounds is presented. Visual attention, which is the cognitive process of selectively concentrating on a region of interest in the visual field, helps human to recognize objects in cluttered natural scenes. The proposed system utilizes a Bayesian model of visual attention to generate a saliency map, and to detect and identify the hand region. Feature based visual attention is implemented using a combination of high level (shape, texture) and low level (color) image features. The shape and texture features are extracted from a skin similarity map, using a computational model of the ventral stream of visual cortex. The skin similarity map, which represents the similarity of each pixel to the human skin color in HSI color space, enhanced the edges and shapes within the skin colored regions. The color features used are the discretized chrominance components in HSI, YCbCr color spaces, and the similarity to skin map. The hand postures are classified using the shape and texture features, with a support vector machines classifier. A new 10 class complex background hand posture dataset namely NUS hand posture dataset-II is developed for testing the proposed algorithm (40 subjects, different ethnicities, various hand sizes, 2750 hand postures and 2000 background images). The algorithm is tested for hand detection and hand posture recognition using 10 fold cross-validation. The experimental results show that the algorithm has a person independent performance, and is reliable against variations in hand sizes and complex backgrounds. The algorithm provided a recognition rate of 94.36 %. A comparison of the proposed algorithm with other existing methods evidences its better performance.read more
Citations
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Journal ArticleDOI
Recent methods and databases in vision-based hand gesture recognition
TL;DR: A review of vision-based hand gesture recognition algorithms reported in the last 16 years using RGB and RGB-D cameras and qualitative and quantitative comparisons of algorithms are provided.
Journal ArticleDOI
Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors
Ming Wang,Zheng Yan,Ting Wang,Pingqiang Cai,Siyu Gao,Yi Zeng,Changjin Wan,Hong Wang,Liang Pan,Jiancan Yu,Shaowu Pan,Ke He,Jie Lu,Xiaodong Chen +13 more
TL;DR: A bioinspired data fusion architecture that can perform human gesture recognition by integrating visual data with somatosensory data from skin-like stretchable strain sensors made from single-walled carbon nanotubes is reported.
Journal ArticleDOI
Hand posture and gesture recognition techniques for virtual reality applications: a survey
TL;DR: A survey on hand posture and gesture is clarified with a detailed comparative analysis of hidden Markov model approach with other classifier techniques, and difficulties and future investigation bearing are also examined.
Journal ArticleDOI
A Deep Convolutional Neural Network Approach for Static Hand Gesture Recognition
V Adithya,R. Rajesh +1 more
TL;DR: A methodology for the recognition of hand gestures, which is the prime component in sign language vocabulary, based on an efficient deep convolutional neural network (CNN) architecture is proposed.
Journal ArticleDOI
HGR-Net: a fusion network for hand gesture segmentation and recognition
TL;DR: In this article, a two-stage CNN architecture is proposed for robust recognition of hand gestures, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture.
References
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