Moch. Hariadi
Teknik Elektro Institut Teknologi Sepuluh November Surabaya, Indonesia

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Journal : Jurnal Ilmiah Kursor

Segmentation of Moving Objects Based on Minkowski Distance Using K-means Clustering Hariadi, Moch.; Mulyanto, Eko; Purnomo, Mauridhi H.; Soeleman, Moch Arief
Kursor In Press Vol 8 no 3
Publisher : University of Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v0i0.1241

Abstract

Segmentation of moving objects is one of the challenging research areas for video surveillance application. The success of object changing position for segmentation is when the moving object completely separate the foreground from its background of frame. It depends on many factors, including the use of suitable clustering method to differentiate the pixels of the foreground and background. This paper propose to use k-means as clustering method for moving object segmentation. The method is evaluated on several distance measures. Several steps are performed to conduct the moving object segmentation, such as frame subtraction, median filtering, and noise removal. These steps are proposed to improve the achievement of moving object segmentation. The performance are evaluated by using Mean of Square Error and Peak Signal to Noise Error. The value of both measurement are 135.02 and 25.52. The experimental result shows that the moving object segmentation performs the best result on Minkowski distance.
IDENTIFIKASI SINYAL ELEKTRODE ENCHEPALO GRAPH UNTUK MENGGERAKKAN KURSOR MENGGUNAKAN TEKNIK SAMPLING DAN JARINGAN SYARAF TIRUAN Hariadi, Moch.; Purnomo, Mauridhi Hery; -, Hindarto
Kursor Vol 6, No 3 (2012)
Publisher : University of Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v6i1.101

Abstract

This paper describe the application of backpropagation neural networks as classification and sampling technique (ST) for the extraction of features from the signal wave Electro Encephalo Graph (EEG). This research aims to develop a system that can recognize the EEG signal that is used to move the cursor. The data used is the EEG data which is IIIA dataset of BCI competition III (BCI Competition III 2003). This data contains data from three subjects: K3b, K6b and L1b. In this study, EEG signal data separated by the imagination of movement to the left, right, leg movements and tongue movements. Decision making has been carried out in two stages. In the first stage, TS is used to extract features from EEG signal data. This feature is as basic inputs in back propagation neural networks as a process of learning. This research used Back Propagation (20-20-10-5-1) and 90 data files EEG signal for the training process. During the identification process into four classes of EEG signal data files data files plus 60 into 150 EEG signal so that the EEG signal data file. The results obtained for the classification of these signals is 80% of the 150 files examined data signal to the process of mapping.