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.
Several computer vision tasks require matching keypoints across several frames or views. The keypoints need to be detected first. Harris and Stephen in @cite_1 proposed the Harris corner detector. This detector considers differential of the corner score into account with reference to direction directly, and has been proved to be accurate in distinguishing between edges and corners.
@cite_1
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The detection of consistent feature points in an image is fundamental for various kinds of computer vision techniques, such as stereo matching, object recognition, target tracking and optical flow computation. This paper presents an event-based approach to the detection of corner points, which benefits from the high temporal resolution, compressed visual information and low latency provided by an asynchronous neuromorphic event-based camera. The proposed method adapts the commonly used Harris corner detector to the event-based data, in which frames are replaced by a stream of asynchronous events produced in response to local light changes at μs temporal resolution. Responding only to changes in its field of view, an event-based camera naturally enhances edges in the scene, simplifying the detection of corner features. We characterised and tested the method on both a controlled pattern and a real scenario, using the dynamic vision sensor (DVS) on the neuromorphic iCub robot. The method detects corners with a typical error distribution within 2 pixels. The error is constant for different motion velocities and directions, indicating a consistent detection across the scene and over time. We achieve a detection rate proportional to speed, higher than frame-based technique for a significant amount of motion in the scene, while also reducing the computational cost.