Hand gesture recognition is very important to communicate in sign language. In this paper, an effective object tracking and hand gesture recognition method is proposed. This method is combination of two well-known approaches, the mean shift and the motion detection algorithm. The mean shift algorithm can track objects based on the color, then when hand passes the face occlusion happens. Several...

In this work, we consider the recognition of dynamic gestures based on representative sub-segments of a gesture, which are denoted as most discriminating segments (MDSs). The automatic extraction and recognition of such small representative segments, rather than extracting and recognizing the full gestures themselves, allows for a more discriminative classifier. A MDS is a sub-segment of a gest...

So far, little is known how the sample assignment of surface electromyogram (sEMG) features in training set influences the recognition efficiency of hand gesture, and the aim of this study is to explore the impact of different sample arrangements in training set on the classification of hand gestures dominated with similar muscle activation patterns. Seven right-handed healthy subjects (24.2 ± ...

Hand Gestures can be identified as the most natural way for Human Computer Interaction as they impersonate how humans interact with each other. In addition to HCI they are used in various applications such as remote control, robot control, human computer interaction, military application and sign language identification. Hand gesture identification is usually implemented in three phases-hand ge...

The recognition of hand gestures from image sequences is an important and challenging problem. This paper presents a robust solution to track and recognize a list of hand gestures from their trajectory. The main tools of the proposed solution are robust kernel density estimation and the related mean shift algorithm, used in both video tracking and trajectory segmentation. The gesture definition...

Continuous gesture spotting is a major topic in human-robot interaction (HRI) research. Human gestures are captured by sensors that provide large amounts of data that can be redundant or incomplete, correlated or uncorrelated. Data dimensionality reduction (DDR) techniques allow to represent such data in a low-dimensional space, making the classification process more efficient. This study demon...

In this paper, we consider a vision-based system that can interpret a user’s gestures in real time to manipulate objects within a medical data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. Static gesture poses are identified to execute non-directional commands. This is accomplished by using Haar-like features t...

In this paper, we consider a vision-based system that can interpret a user’s gestures in real time to manipulate objects within a medical data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. Static gesture poses are identified to execute nondirectional commands. This is accomplished by using Haar-like features to...

hand gesture recognition is very important to communicate in sign language. in this paper, an effective object tracking and hand gesture recognition method is proposed. this method is combination of two well-known approaches, the mean shift and the motion detection algorithm. the mean shift algorithm can track objects based on the color, then when hand passes the face occlusion happens. several...

This paper surveys a new research field of object behavior classification using sequential syntactic pattern recognition, which recognizes high-level object behaviors while in parallel recovering from low-level object recognition classification errors. A new approach of syntactical object behavior classification with a robust implementation is introduced. It is an innovative approach that requi...