Enhanced rotational feature points matching using orientation correction

In matching between images, several techniques have been developed particularly for estimating orientation assignment in order to make feature points invariant to rotation. However, imperfect estimation of the orientation assignment may lead to feature mismatching and a low number of correctly matched points. Additionally, several possible candidates with high correlation values for one feature in the reference image may lead to matching confusion. In this paper, we propose a post-processing matching technique that will not only increase the number of correctly matched points but also manage to solve the above mentioned two issues. The key idea is to modify feature orientation based on the relative rotational degree between two images, obtained by taking the difference between the major correctly matched points in the first matching cycle. From the analysis, our proposed method shows that the number of detected points correctly matched with the reference image can be increased by up to 50%. In addition, some mismatched points due to similar correlation values in the first matching round can be corrected. Another advantage of the proposed algorithm it that it can be applied to other state-of-the-art orientation assignment techniques

Enhanced rotational feature points matching using orientation correction

In matching between images, several techniques have been developed particularly for estimating orientation assignment in order to make feature points invariant to rotation. However, imperfect estimation of the orientation assignment may lead to feature mismatching and a low number of correctly matched points. Additionally, several possible candidates with high correlation values for one feature in the reference image may lead to matching confusion. In this paper, we propose a post-processing matching technique that will not only increase the number of correctly matched points but also manage to solve the above mentioned two issues. The key idea is to modify feature orientation based on the relative rotational degree between two images, obtained by taking the difference between the major correctly matched points in the first matching cycle. From the analysis, our proposed method shows that the number of detected points correctly matched with the reference image can be increased by up to 50%. In addition, some mismatched points due to similar correlation values in the first matching round can be corrected. Another advantage of the proposed algorithm it that it can be applied to other state-of-the-art orientation assignment techniques

Blood vessel segmentation and modeling in fundus images

Studie cévního stromu v obrazech sítnice mají využití nejen na poli medicíny, ale také biometrie. Matematický popis cévního řečiště sítnice by mohl pomoci k ulehčení a vylepšení diagnóz některých chorob, k jejich automatické lokalizaci nebo k urychlení identifikace a verifikace osob. Cílem práce je navrhnout a vytvořit algoritmus, který bude automaticky aproximovat hlavní retinální cévy parabolickou, lineární a kvartickou funkcí. Hlavní část práce je tedy věnována této problematice včetně segmentace cévního řečiště pomocí Gaborových filtrů, morfologické eroze, prahování, skeletonizace, a výsledné optimalizace aproximačního modelu. Na závěr je diskutována kvalita vytvořeného modelovacího algoritmu.Studies of the vascular tree in the retina have applications not only in the medical field but also biometrics. The mathematical description of the retinal vasculature could help facilitate and improve the diagnosis of certain diseases, their automatic localization or to accelerate the identification and verification of individuals. The aim is to design and develop an algorithm that will automatically approximate major retinal vessels by parabolic, linear and kvartic functions. The main part of this thesis is therefore devoted to this issue, including vascular segmentation using Gabor filters, morphological erosion, thresholding, skeletonization and the resulting optimization of the approximation model. The quality of the produced algorithm is discussed in the summary.