DOI: https://doi.org/10.14710/jtsiskom.6.1.2018.32-36

Maximum Likelihood Classification dengan Ekstraksi Fitur Fast Fourier Transform untuk Pengenalan Mobil

Maximum Likelihood Classification with Fast Fourier Transform Feature Extraction for Car Recognition

*Derry Alamsyah  -  Department of Informatics, STMIK Global Informatika MDP Palembang, Indonesia
Received: 3 Dec 2017; Published: 31 Jan 2018.
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Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

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Abstract

The car recognition is part of the field of traffic surveillance on the image. In general, the car recognition using the form-based feature as a unique feature. Another feature in object recognition is the frequency feature. One feature of frequency is the Fourier feature, this feature is obtained by using Fast Fourier Transform (FFT) method. The object recognition can be done by determining the maximum value of likelihood and classifying it with Maximum Likelihood Classification (MLC). The use of FFT and MLC in the car object recognition has never been used. The results of both are in a good accuracy that is 76%.

Keywords: Fast fourier transform; maximum likelihood classification; car recognition

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Last update: 2021-03-08 04:37:52

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