Main Article Content


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.

Article Details

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.


  1. Yang, Lin, Longyu Zhang, Haiwei Dong, Abdulhameed Alelaiwi, and Abdulmotaleb El Saddik. "Evaluating and improving the depth accuracy of Kinect for Windows v2." IEEE Sensors Journal 15, no. 8 (2015): 4275-4285.
  2. Jais, Hairina Mohd, Zainal Rasyid Mahayuddin, and Haslina Arshad. "A review on gesture recognition using Kinect." In Electrical Engineering and Informatics (ICEEI), 2015 International Conference on, pp. 594-599. IEEE, 2015.
  3. Hamissi, Minoo, and Karim Faez. "Real-time hand gesture recognition based on the depth map for human-robot interaction." International Journal of Electrical and Computer Engineering 3, no. 6 (2013): 770.
  4. Wachs, Juan P., Helman Stern, and Yael Edan. "Cluster labeling and parameter estimation for the automated setup of a hand-gesture recognition system." IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 35, no. 6 (2005): 932-944.
  5. Gholami, Farnood, Daria A. Trojan, József Kövecses, Wassim M. Haddad, and Behnood Gholami. "A Microsoft Kinect-based point-of-care gait assessment framework for multiple sclerosis patients." IEEE journal of biomedical and health informatics 21, no. 5 (2017): 1376-1385.
  6. Dehbandi, Behdad, Alexandre Barachant, David Harary, John Davis Long, K. Zoe Tsagaris, Silverio Joseph Bumanlag, Victor He, and David Putrino. "Using data from the Microsoft Kinect 2 to quantify upper limb behavior: a feasibility study." IEEE journal of biomedical and health informatics 21, no. 5 (2017): 1386-1392.
  7. Anwer, Atif, Syed Saad Azhar Ali, Amjad Khan, and Fabrice Mériaudeau. "Underwater 3-D Scene Reconstruction Using Kinect v2 Based on Physical Models for Refraction and Time of Flight Correction." IEEE Access 5 (2017): 15960-15970.
  8. Yohannes, Ervin, and Fitri Utaminingrum. "Building Segmentation of Satellite Image based on Area and Perimeter using Region Growing." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 3 (2016): 579-585.
  9. Jyothirmayi, Tayi, K. Srinivasa Rao, Peri Srinivasa Rao, and Ch Satyanarayana. "Image Segmentation Based on Doubly Truncated Generalized Laplace Mixture Model and K Means Clustering." International Journal of Electrical and Computer Engineering 6, no. 5 (2016): 2188.
  10. Chandana, BSai, K. Srinivas, and R. Kiran Kumar. "Clustering algorithm combined with hill climbing for classification of remote sensing image." International Journal of Electrical and Computer Engineering 4, no. 6 (2014): 923.
  11. Kazemi-Beydokhti, Mohammad, Rahim Ali Abbaspour, and Masoud Mojarab. "Spatio-Temporal Modeling of Seismic Provinces of Iran Using DBSCAN Algorithm." Pure and Applied Geophysics 174, no. 5 (2017): 1937-1952.
  12. Utaminingrum, F., Mufarroha, F.A.†Hand Gesture Recognition using Adaptive Network Based Fuzzy Inference System and K-Nearest Neighbor.†International Journal of Technology (IJTech), vol. 8 no.3 (2017): pp. 559-567
  13. Hou, Jian, Huijun Gao, and Xuelong Li. "Dsets-dbscan: a parameter-free clustering algorithm." IEEE Transactions on Image Processing 25, no. 7 (2016): 3182-3193.
  14. Edla, Damodar Reddy, Prasanta K. Jana, and IEEE Senior Member. "A prototype-based modified DBSCAN for gene clustering." Procedia Technology 6 (2012): 485-492.
  15. Kumar, K. Mahesh, and A. Rama Mohan Reddy. "A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method." Pattern Recognition 58 (2016): 39-48.
  16. Dudik, Joshua M., Atsuko Kurosu, James L. Coyle, and Ervin Sejdić. "A comparative analysis of DBSCAN, K-means, and quadratic variation algorithms for automatic identification of swallows from swallowing accelerometry signals." Computers in biology and medicine 59 (2015): 10-18.
  17. Shen, Jianbing, Xiaopeng Hao, Zhiyuan Liang, Yu Liu, Wenguan Wang, and Ling Shao. "Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm." IEEE Transactions on Image Processing 25, no. 12 (2016): 5933-5942.