Clustering of Human Hand on Depth Image using DBSCAN Method




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.


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How to Cite

Yohannes, E., Utaminingrum, F., & Shih, T. K. (2019). Clustering of Human Hand on Depth Image using DBSCAN Method. Journal of Information Technology and Computer Science, 4(2), 177–184.