Napredna pretraga

Pregled bibliografske jedinice broj: 1028055

On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods


Bojanić, David; Bartol, Kristijan; Pribanić, Tomislav; Petković, Tomislav; Diez Donoso, Yago; Salvi Mas, Joaquim
On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods // 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) / Lončarić, Sven ; Bregović, Robert ; Carli, Marco ; Subašić, Marko (ur.).
Dubrovnik: IEEE, 2019. str. 64-69 doi:10.1109/ISPA.2019.8868792 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods

Autori
Bojanić, David ; Bartol, Kristijan ; Pribanić, Tomislav ; Petković, Tomislav ; Diez Donoso, Yago ; Salvi Mas, Joaquim

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) / Lončarić, Sven ; Bregović, Robert ; Carli, Marco ; Subašić, Marko - Dubrovnik : IEEE, 2019, 64-69

ISBN
978-1-7281-3140-5

Skup
11th International Symposium on Image and Signal Processing and Analysis

Mjesto i datum
Dubrovnik, Hrvatska, 23-25.09.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Keypoint detection, keypoint description, deep learning, benchmark evaluation, average precision

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2018-01-8118 - Izračun antropometrijskih mjera pametnim telefonom i tabletom (Tomislav Pribanić, )
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (Sven Lončarić, EK)

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

Citati