Hand posture recognition is an essential module in applications such as human-computer interaction (HCI), games, and sign language systems, in which performance and robustness are the primary requirements. In this paper, we proposed automatic classification to recognize 21 hand postures that represent letters in Thai finger-spelling based on Histogram of Orientation Gradient (HOG) feature (whic...

In this study we introduce and demystify a novel signal pattern recognition method, Spectral Collaborative Representation based Classification (SCRC) and demonstrate its application for recognition of hand gestures and postures using Electromyography sensors. A recently released Thalmic Labs MYO armband is used to gather muscle electromyography signals. Along with the new signal pattern classif...

The use of gesture as a natural interface serves as a motivating force for research in modeling, analyzing and recognition of gestures. In particular, human computer intelligent interaction needs vision-based gesture recognition, which involves many interdisciplinary studies. A survey on recent vision-based gesture recognition approaches is given in this paper. We shall review methods of static...

The automatic interpretation of human gestures can be used for a natural interaction with computers without the use of mechanical devices such as keyboards and mice. The recognition of hand postures have been studied for many years. However, most of the literature in this area has considered 2D images which cannot provide a full description of the hand gestures. In addition, a rotation-invarian...

In RoboCup Middle Size league (MSL) the main referee uses assisting technology, controlled by a second referee, to support him, in particular for conveying referee decisions for robot players with the help of a wireless communication system. In this paper a vision-based system is introduced, able to interpret dynamic and static gestures of the referee, thus eliminating the need for a second one...

The performance of Hand Gesture Recognition (HGR) depends on the hand shape. Segmentation helps in recognition gestures for more accuracy and improves overall compared to other existing deep neural networks. crucial segmentation task is extremely complicated because background complexity, variation illumination etc. proposed modified UNET ensemble model Convolutional Neural Networks (CNN) under...

This paper presents an approach for simultaneous tracking and recognition of hierarchical object representations in terms of multiscale image features. A scale-invariant dissimilarity measure is proposed for comparing scale-space features at different positions and scales. Based on this measure, the likelihood of hierarchical, parameterized models can be evaluated in such a way that maximizatio...