Document Update

SIBGRAPI Digital Library Archive

2023 accepted paper update form

The submission system will allow any upgrade until August 16th, 2023.
If you have any question, please contact: thales.korting@inpe.br


Field Name		Field Value
Reference Type		Conference Paper (Conference Proceedings)
Work Title	(*)(?)
Short Title
	Don't forget to press the "+" button to enter the co-author data, if any.		v
1^st Author	(*)(?)
Affiliation (1^st Author)	(*)
2^nd Author	(*)(?)
Affiliation (2^nd Author)	(*)
EMailAddress
Keywords	(*)(?)
Abstract	(*)(?)	In ophthalmology and vision science applications, aligning a pair of retinal images is of paramount importance to support disease diagnosis and routine eye examinations. This paper introduces an end-to-end framework capable of learning the registration task in a fully unsupervised manner. The proposed approach combines Convolutional Neural Networks and Spatial Transformer Network into a unified pipeline that incorporates a similarity metric to gauge the difference between the images, enabling image alignment without requiring any ground-truth data. The validation study demonstrates that the model can successfully deal with several categories of fundus images, surpassing other recent techniques for retinal registration.
Language	(*)
File Name (attach here your file)	(?)
e-Mail (login)		giovana.a.benvenuto@unesp.br