Mozaiklemede geometrik bozulma düzeltme ve göz verilerinde uygulanması.

Image processing is used in many sectors in parallel with the developing software and hardware technology. Differences are observed in the image techniques used according to the needs of the sectors. In this context, very precise measurements and powerful algorithms are required to be used in the field of health. In this article, an image mosaic was made on retinal images using datasets such as D-EYE and FIRE. Feature detection, feature matching, image matching, image wrapping and image mosaicing processes are applied on the images. Harris corner detector was used to find the corners in the image. The positions of the points in the 2 corresponding images were found with the corner detector. Scale-Invariant Feature Transform (SIFT) was used as the feature recognition algorithm between the coordinates. Feature and image matching processes were performed between the determined features. Weak spots are eliminated with the help of Random Sample Consensus (RANSAC). The homography is defined for the projective transformation with the remaining points. In the last step, a geometric transformation was made with the homography matrix. The image mosaic application in our study; It can be applied to a wide area, especially retinal images, unmanned aerial vehicle images, bacteria or tomography images. It has been tested with different data sets and a successful result has been obtained. [ABSTRACT FROM AUTHOR]