A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT) [17] , Speed-up Robust Feature (SURF) [4] , and more recently Binary Robust Invariant Scalable Keypoints (BRISK) [16] to name a few. These days, the deployment of vision algorithms on smart phones and embedded devices with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster to compute, more compact while remaining robust to scale, rotation and noise. To best address the current requirements, we propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Keypoint (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. Our experiments show that FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are thus competitive alternatives to existing keypoints in particular for embedded applications.
Inspired by the human visual system, fast retina keypoint REF uses the learning strategy of ORB with a DAISYlike sampling pattern.
FREAK: Fast Retina Keypoint
2012 IEEE Conference on Computer Vision and Pattern Recognition
2012 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science