BRISK: Binary Robust invariant scalable keypoints

Leutenegger, Stefan; Chli, Margarita; Siegwart, Roland

doi:10.1109/iccv.2011.6126542

Cited by 2,921 publications

(1,917 citation statements)

References 14 publications

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“…-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.…”

Section: Figmentioning

confidence: 99%

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Instance detection by keypoint matching beyond the nearest neighbor

Uzyıldırım

Özuysal

2016

SIViP

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The binary descriptors are the representation of choice for real-time keypoint matching. However, they suffer from reduced matching rates due to their discrete nature. We propose an approach that can augment their performance by searching in the top K near neighbor matches instead of just the single nearest neighbor one. To pick the correct match out of the K near neighbors, we exploit statistics of descriptor variations collected for each keypoint in an off-line training phase. This is a similar approach to those that learn a patch specific keypoint representation. Unlike these approaches, we only use a keypoint specific score to rank the list of K near neighbors. Since this list can be efficiently computed with approximate nearest neighbor algorithms, our approach scales well to large descriptor sets.

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Section: Figmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Instance detection by keypoint matching beyond the nearest neighbor

Uzyıldırım

Özuysal

2016

SIViP

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“…There are also several features having their own detectors and descriptors, such as SIFT (Lowe, 1999) or BRISKs (binary robust invariant scalable keypoints) (Leutenegger et al, 2011). The latter possess the so-called "binary descriptor"-a vector of boolean values instead of real or integer numbers.…”

Section: 2mentioning

confidence: 99%

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

Wilkowski

Kornuta

Stefańczyk

et al. 2016

International Journal of Applied Mathematics and Computer Science

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The article focuses on the problem of building dense 3D occupancy maps using commercial RGB-D sensors and the SLAM approach. In particular, it addresses the problem of 3D map representations, which must be able both to store millions of points and to offer efficient update mechanisms. The proposed solution consists of two such key elements, visual odometry and surfel-based mapping, but it contains substantial improvements: storing the surfel maps in octree form and utilizing a frustum culling-based method to accelerate the map update step. The performed experiments verify the usefulness and efficiency of the developed system.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Oszust

2016

SIViP

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The desired local feature descriptor should be distinctive, compact and fast to compute and match. Therefore, many computer vision applications use binary keypoint descriptors instead of floating-point, rich techniques. In this paper, an optimisation approach to the design of a binary descriptor is proposed, in which the detected keypoint is described using several, scale-dependent patches. Each such patch is divided into disjoint blocks of pixels, and then, binary tests between blocks' intensities, as well as their gradients, are used to obtain the binary string. Since the number of image patches and their relative sizes influence the descriptor creation pipeline, a simulated annealing algorithm is used to determine them, optimising recall and precision of keypoint matching. The simulated annealing is also used for dimensionality reduction in long binary strings. The proposed approach is extensively evaluated and compared with SIFT, SURF and BRIEF on public benchmarks. Obtained results show that the binary descriptor created using the resulted pipeline is faster to compute and yields comparable or better performance than the state-of-the-art descriptors under different image transformations.

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BRISK: Binary Robust invariant scalable keypoints

Cited by 2,921 publications

References 14 publications

Instance detection by keypoint matching beyond the nearest neighbor

Instance detection by keypoint matching beyond the nearest neighbor

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

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

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