TELKOMNIKA Telecommunication, Computing, Electronics and Control
Matching algorithm performance analysis for autocalibration method of stereo vision

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Matching algorithm performance analysis for autocalibration method of stereo vision

Autocalibration, Image matching, Stereo vision

Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods.

Raden Arief Setyawan, Rudy Soenoko, Moch Agus Choiron, Panca Mudjirahardjo

DOI: 10.12928/TELKOMNIKA.v18i2.14842

Universitas Ahmad Dahlan

April 2020

Sri Wahyuni

ISSN: 1693-6930

http://journal.uad.ac.id/index.php/TELKOMNIKA

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TELKOMNIKA Telecommunication, Computing, Electronics and Control