On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods (CROSBI ID 682518)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Bojanić, David ; Bartol, Kristijan ; Pribanić, Tomislav ; Petković, Tomislav ; Diez Donoso, Yago ; Salvi Mas, Joaquim
engleski
On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods
The purpose of this study is to give a performance comparison between several classic hand-crafted and deep keypoint detector and descriptor methods. In particular, we consider the following classical algorithms: SIFT, SURF, ORB, FAST, BRISK, MSER, HARRIS, KAZE, AKAZE, AGAST, GFTT, FREAK, BRIEF and RootSIFT, where a subset of all combinations is paired into detector-descriptor pipelines. Additionally, we analyze the performance of two recent and perspective deep detector-descriptor models, LF- Net and SuperPoint. Our benchmark relies on the HPSequences dataset that provides real and diverse images under various geometric and illumination changes. We analyze the performance on three evaluation tasks: keypoint verification, image matching and keypoint retrieval. The results show that certain classic and deep approaches are still comparable, with some classic detector-descriptor combinations overperforming pretrained deep models. In terms of the execution times of tested implementations, SuperPoint model is the fastest, followed by ORB.
keypoint detection, keypoint description, deep learning, benchmark evaluation, average precision
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Podaci o prilogu
64-69.
2019.
objavljeno
10.1109/ISPA.2019.8868792
Podaci o matičnoj publikaciji
2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)
Lončarić, Sven ; Bregović, Robert ; Carli, Marco ; Subašić, Marko
Dubrovnik: Institute of Electrical and Electronics Engineers (IEEE)
978-1-7281-3140-5
1849-2266
Podaci o skupu
11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)
predavanje
23.09.2019-25.09.2019
Dubrovnik, Hrvatska