Clustering of Human Hand on Depth Image using DBSCAN Method

Abstract

In recent years, depth images are popular research in image processing, especially in clustering field. The depth image can capture by depth cameras such as Kinect, Intel Real Sense, Leap Motion, and etc. Many objects and methods can be implemented in clustering field and issues. One of popular object is human hand since has many functions and important parts of human body for daily routines. Besides, the clustering method has been developed for any goal and even combine with another method. One of clustering method is Density-Based Spatial Clustering of Applications with Noise (DBSCAN) which automatic clustering method consists of minimum points and epsilon. Define the epsilon in DBSCAN is important thing since the result depends on those. We want to look for the best epsilon for clustering human hand in the depth images. We selected the epsilon from 5 until 100 for getting the best clustering results. Moreover, those epsilons will be testing in three distance to get accurate results.