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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Information Technology and Computer Science
Timothy K. Shih
National Central University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Clustering of Human Hand on Depth Image using DBSCAN Method Ervin Yohannes; Fitri Utaminingrum; Timothy K. Shih
Journal of Information Technology and Computer Science Vol. 4 No. 2: September 2019
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1131.492 KB) | DOI: 10.25126/jitecs.201942133

Abstract

In recent years, depth images are popular research in imageprocessing, especially in clustering field. The depth image can captureby depth cameras such as Kinect, Intel Real Sense, Leap Motion, and etc.Many objects and methods can be implemented in clustering field andissues. One of popular object is human hand since has many functionsand important parts of human body for daily routines. Besides, theclustering method has been developed for any goal and even combinewith another method. One of clustering method is Density-Based SpatialClustering of Applications with Noise (DBSCAN) which automaticclustering method consists of minimum points and epsilon. Define theepsilon in DBSCAN is important thing since the result depends on those.We want to look for the best epsilon for clustering human hand in thedepth images. We selected the epsilon from 5 until 100 for getting thebest clustering results. Moreover, those epsilons will be testing in threedistance to get accurate results.
Error Action Recognition on Playing The Erhu Musical Instrument Using Hybrid Classification Method with 3D-CNN and LSTM Aditya Permana; Timothy K. Shih; Aina Musdholifah; Anny Kartika Sari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.76555

Abstract

Erhu is a stringed instrument originating from China. In playing this instrument, there are rules on how to position the player's body and hold the instrument correctly. Therefore, a system is needed that can detect every movement of the Erhu player. This study will discuss action recognition on video using the 3DCNN and LSTM methods. The 3D Convolutional Neural Network method is a method that has a CNN base. To improve the ability to capture every information stored in every movement, combining an LSTM layer in the 3D-CNN model is necessary. LSTM is capable of handling the vanishing gradient problem faced by RNN. This research uses RGB video as a dataset, and there are three main parts in preprocessing and feature extraction. The three main parts are the body, erhu pole, and bow. To perform preprocessing and feature extraction, this study uses a body landmark to perform preprocessing and feature extraction on the body segment. In contrast, the erhu and bow segments use the Hough Lines algorithm. Furthermore, for the classification process, we propose two algorithms, namely, traditional algorithm and deep learning algorithm. These two-classification algorithms will produce an error message output from every movement of the erhu player.
Co-Authors Aditya Permana Aina Musdholifah Anny Kartika Sari Ervin Yohannes Utaminingrum, Fitri