MREAK : Morphological Retina Keypoint Descriptor
A variety of computer vision applications depend on the efficiency of image matching algorithms used. Various descriptors are designed to detect and match features in images. Deployment of this algorithms in mobile applications creates a need for low computation time. Binary descriptors requires less computation time than float-point based descriptors because of the intensity comparison between pairs of sample points and comparing after creating a binary string. In order to decrease time complexity, quality of keypoints matched is often compromised. We propose a keypoint descriptor named Morphological Retina Keypoint Descriptor (MREAK) inspired by the function of human pupil which dilates and constricts responding to the amount of light. By using morphological operators of opening and closing and modifying the retinal sampling pattern accordingly, an increase in the number of accurately matched keypoints is observed. Our results show that matched keypoints are more efficient than FREAK descriptor and requires low computation time than various descriptors like SIFT, BRISK and SURF.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching
For retinal image matching (RIM), we propose SuperRetina, the first end-to-end method with jointly trainable keypoint detector and descriptor. SuperRetina is trained in a novel semi-supervised manner. A small set of (nea…
Image RegistrationTripletConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration
Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration.…
Contrastive LearningImage RegistrationTripletUnsupervised training of keypoint-agnostic descriptors for flexible retinal image registration
Current color fundus image registration approaches are limited, among other things, by the lack of labeled data, which is even more significant in the medical domain, motivating the use of unsupervised learning. Therefor…
Image RegistrationKeypoint DetectionUnsupervised Deep Learning-based Keypoint Localization Estimating Descriptor Matching Performance
Retinal image registration, particularly for color fundus images, is a challenging yet essential task with diverse clinical applications. Existing registration methods for color fundus images typically rely on keypoints …
Deep LearningImage RegistrationKeypoint DetectionA Keypoint Detection and Description Network Based on the Vessel Structure for Multi-Modal Retinal Image Registration
Ophthalmological imaging utilizes different imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography. Multiple images with different modalities or ac…
Image RegistrationKeypoint Detection