Performance of Interpolated Histogram of Oriented Gradients on the Feature Calculation of SIFT
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Univ Suceava, Fac Electrical Eng
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Scale Invariant Feature Transform (SIFT) is the most dominant and robust object detection algorithm. It utilizes the Histogram of Oriented Gradients (HOG) method for feature computation. HOG is applied with trilinear interpolation to gain performance improvement. This paper examines the effect of interpolation on the performance of SIFT on both OXFORD and HPatches datasets. The various algorithms of interpolation for HOG, and the spatial binning process algorithm, are presented here. The performance is evaluated with Intersection Over Union, Correct Match Percentage, as well as the execution time of the algorithms. Moreover, we used the multiplication of the Intersection Over Union and Correct Match Percentage to take advantage of both metrics. It was observed that the interpolation did not significantly affect the performance of the SIFT.
Açıklama
Anahtar Kelimeler
mage processing, computer vision, image analysis, feature extraction, object detection
Kaynak
Advances in Electrical and Computer Engineering
WoS Q Değeri
Q4
Scopus Q Değeri
Cilt
22
Sayı
3
