MREAK : Morphological Retina Keypoint Descriptor
This work addresses the need for efficient image matching in mobile applications, though it appears incremental as it builds upon existing binary descriptor concepts.
The paper tackles the problem of balancing computational efficiency and matching accuracy in binary keypoint descriptors for mobile computer vision applications, proposing MREAK which uses morphological operators inspired by the human pupil to increase accurately matched keypoints while maintaining low computation time compared to descriptors like FREAK, SIFT, BRISK, and SURF.
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.