Rotation Invariant Feature Extraction of Handwritten Signature Images
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper presents extraction of rotation invariant features of handwritten signature images for classification. Initially, curvelet transform was applied to all signature image in the database. The mean and standard deviation values of each curvelet sub bands were used as features. Rotation invariance was obtained by applying cycle shift around the total spectral energy values of curvelet sub bands. Then, the classification process was carried out with the obtained features. The performance was compared with the method without cycle shift. As a result, it has been determined that the presented method gives the most successful accuracy using the support vector machine classifier. © 2021 IEEE.
Açıklama
IEEE SMC Society; IEEE Turkey Section
2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021 -- 6 October 2021 through 8 October 2021 -- Elazig -- 174400
2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021 -- 6 October 2021 through 8 October 2021 -- Elazig -- 174400
Anahtar Kelimeler
curvelet transform, image classification, rotation invariant feature extraction
Kaynak
Proceedings - 2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021
WoS Q Değeri
Scopus Q Değeri
N/A
