# MREAK : Morphological Retina Keypoint Descriptor

## TL;DR

This paper introduces MREAK, a low-computation binary keypoint descriptor inspired by the human retina, which improves matching accuracy and efficiency in image processing tasks, especially suitable for mobile applications.

## Contribution

MREAK is a novel morphological retinal-inspired descriptor that enhances keypoint matching accuracy while reducing computational complexity compared to existing descriptors.

## Key findings

- MREAK outperforms FREAK in keypoint matching accuracy.
- MREAK requires less computation time than SIFT, BRISK, and SURF.
- MREAK increases the number of accurately matched keypoints.

## Abstract

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.