Comparative Analysis of the Feature Extraction Performance of  Augmented Reality Algorithms

Umut TOSUN

doi:https://doi.org/10.17776/csj.638297

Makale detayları

Comparative Analysis of the Feature Extraction Performance of Augmented Reality Algorithms

Yazarlar:

Umut TOSUN

Ana Başlıklar:

Astronomi ve ilgili bilimler

Dergi:

Cumhuriyet Science Journal 2019 Vol.40 Issue 4, pp.958-966

Belge Tipi:

Makale

Yayın Tarihi:

12/2019

Belge Dili:

Türkçe

Document ID (Org No.):

2000920497

DOI:

https://doi.org/10.17776/csj.638297

Özet:

 


The algorithms that extract keypoints
and descriptors in augmented reality applications are getting more and more
important  in terms of performance.
Criterions like time and correct matching of points gain more impact according
to the type of application. In this paper, the performance of the algorithms
used to identify an image using keypoint and descriptor extraction is  studied. In the context of this research,
main criterion like the number of keypoints and descriptors that the algorithms
extract, algorithm execution time, and the quality of keypoints and descriptors
extracted are considered as the performance metrics. Same data stacks were used
for obtaining comparison results. In addition to comparisons for a group of
well-known augmented reality applications, the best performing algorithms for
varying applications were also suggested. C++ language and OpenCV library were
used for the implementation of the augmented reality algorithms compared.


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Makale detayları

Comparative Analysis of the Feature Extraction Performance of Augmented Reality Algorithms

Yazarlar: Umut TOSUN

Ana Başlıklar: Astronomi ve ilgili bilimler

Dergi: Cumhuriyet Science Journal 2019 Vol.40 Issue 4, pp.958-966

Belge Tipi: Makale

Yayın Tarihi: 12/2019

Belge Dili: Türkçe

Document ID (Org No.): 2000920497

DOI: https://doi.org/10.17776/csj.638297

Sayı

Yazarlar:

Umut TOSUN

Ana Başlıklar:

Astronomi ve ilgili bilimler

Dergi:

Cumhuriyet Science Journal 2019 Vol.40 Issue 4, pp.958-966

Belge Tipi:

Makale

Yayın Tarihi:

12/2019

Belge Dili:

Türkçe

Document ID (Org No.):

2000920497

DOI:

https://doi.org/10.17776/csj.638297