We present a novel means of describing local image appearances using binary strings. Binary descriptors have drawn increasing interest in recent years due to their speed and low memory footprint. A known shortcoming of these representations is their inferior performance compared to larger, histogram based descriptors such as the SIFT. Our goal is to close this performance gap while maintaining the benefits attributed to binary representations. To this end we propose the Learned Arrangements of Three Patch Codes descriptors, or LATCH. Our key observation is that existing binary descriptors are at an increased risk from noise and local appearance variations. This, as they compare the values of pixel pairs; changes to either of the pixels can easily lead to changes in descriptor values, hence damaging its performance. In order to provide more robustness, we instead propose a novel means of comparing pixel patches. This ostensibly small change, requires a substantial redesign of the descriptors themselves and how they are produced. Our resulting LATCH representation is rigorously compared to state-of-the-art binary descriptors and shown to provide far better performance for similar computation and space requirements.
Rather than random sampling or unsupervised learning of pairs, the Binary Robust Invariant Scalable Keypoints (BRISK) @cite_7 use hand-crafted, concentric ring-based sampling patterns. BRISK uses pixel pairs with large distances between them to compute the patch orientation, and pixel pairs separated by short distances to compute the values of the descriptor itself, again, by performing binary intensity comparisons on pixel pairs. More recently, inspired by the retinal patterns of the human eye, the Fast REtinA Keypoint descriptor (FREAK) was proposed. Similarly to BRISK, FREAK also uses a concentric rings arrangement, but unlike it, FREAK samples exponentially more points in the inner rings. Of all the possible pairs which may be sampled under these guidelines, FREAK, following ORB, uses unsupervised learning to choose an optimal set of point pairs.
@cite_7
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Effective and efficient generation of keypoints from an image is a well-studied problem in the literature and forms the basis of numerous Computer Vision applications. Established leaders in the field are the SIFT and SURF algorithms which exhibit great performance under a variety of image transformations, with SURF in particular considered as the most computationally efficient amongst the high-performance methods to date. In this paper we propose BRISK1, a novel method for keypoint detection, description and matching. A comprehensive evaluation on benchmark datasets reveals BRISK's adaptive, high quality performance as in state-of-the-art algorithms, albeit at a dramatically lower computational cost (an order of magnitude faster than SURF in cases). The key to speed lies in the application of a novel scale-space FAST-based detector in combination with the assembly of a bit-string descriptor from intensity comparisons retrieved by dedicated sampling of each keypoint neighborhood.