Elliptical density shape model for hand gesture recognition

Abstract

Recently, the Microsoft Kinect sensor has provided the whole new type of data in computer vision, the depth information. The most important contribution of depth information is to overcome one of the hardest parts in visual information extraction, the segmentation process. Especially in human action recognition field, the depth data help reduce the noise and variance of background and illumination of the real world environment. But beside that, most of state-of-the-art approaches are still using the complex feature representation with quite long feature vectors and they lead to many other tasks to do to reduce the complexity of the whole system model. In this paper, we want to solve this problem using the Elliptical Density Shape (EDS) model that could provide the simplified geometric shape feature of any complex shape object through time sequences but still robust enough when applying in the recognition process.

Method of Multi-Modal Video Analysis of Hand Movements for Automatic Recognition of Isolated Signs of Russian Sign Language

“…For comparison, the following methods were selected: 1) the Restricted Graph-Based Genetic Programming (hereinafter RGGP) (Liu et. al., 2013); 2) the Elliptical Density Shape Model (hereinafter EDS) (Tung et. al., 2014); 3) the Multi-Stream Recurrent Neural Network (hereinafter MRNN) (Nishida et.…”