FREAK: Fast Retina Keypoint

Abstract

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.

Instance detection by keypoint matching beyond the nearest neighbor

“…-We demonstrate that, despite learning a separate representation for each individual keypoint similar to [3,8,9,19], our approach does not require a brute-force search in a large descriptor set when coupled with the LSH [2] approach to compute the list of K near neighbors. -We show that our approach is relatively descriptor independent and it extends the matching range of several binary descriptors: BRIEF [5], ORB [21], BRISK [10], FREAK [1], and LATCH [11], which has recently been shown to outperform state-of-the-art binary descriptors.…”

“…The binary descriptors (such as [1,5,10,11,18,21,22]) are extensively used in real-time applications for object detection This work was supported by the TÜBİTAK project 113E496.…”

An optimisation approach to the design of a fast, compact and distinctive binary descriptor

“…In spite of introduced improvements, ORB performs better than BRIEF only in presence of large rotation or scale change [17]. In Fast Retina Keypoint (FREAK) [7], the sampling pattern was inspired by the human visual system. Here, the learning process is similar to ORB's with additional rejection of correlated pairs.…”

“…Therefore, binary descriptors have become more attractive in recent years, since they are compact and faster to compare using Hamming metric. In most cases, handcrafted binary descriptors are obtained using pairwise tests between intensities of predefined parts of described image patch, i.e., pixels or regions according to a sampling pattern [4][5][6][7][8]. However, binary descriptors can be long, what requires an additional procedure for their reduction.…”

Efficient generation of 3D surfel maps using RGB–D sensors

“…Recently proposed detectors locating key points in RGB images include the FAST (features from accelerated segment test) (Rosten and Drummond, 2006) and the AGAST (adaptive and generic accelerated segment test) (Mair et al, 2010)), whereas sample novel descriptors are BRIEFs (binary robust independent elementary features) (Calonder et al, 2010) and the FREAK (fast retina keypoint) (Alahi et al, 2012).…”