Speeded-Up Robust Features (SURF)

@article{Bay2008SpeededUpRF,
  title={Speeded-Up Robust Features (SURF)},
  author={Herbert Bay and Andreas Ess and Tinne Tuytelaars and Luc Van Gool},
  journal={Comput. Vis. Image Underst.},
  year={2008},
  volume={110},
  pages={346-359}
}

A new scale invariant feature detector and modified SURF descriptor

TLDR
A new scale invariant octagonal center-surround detector, named OCT, and a modified SURF descriptor named I-SURF descriptor are introduced, which have better performance than SURF and SIFT.

An Evaluation of Open Source SURF Implementations

TLDR
Different SURF implementations written in C++ are evaluated and it is found that some implementations produce up to 33% lower repeatability and up to 44% lower maximum recall than the original implementation, while the implementation provided with the software Pan-o-matic produced almost identical results.

Gauge-SURF descriptors

C-SURF: Colored Speeded Up Robust Features

TLDR
The built C-SURF (Colored Speeded Up Robust Features) is more robust than the conventional SURF with respect to rotation variations and uses 112 dimensions to describe not only the distribution of Harr-wavelet responses but also the color information within the interest point neighborhood.

Fast Feature Matching by Coarse-to-Fine Comparison of Rearranged SURF Descriptors

TLDR
A fast matching method that rearranges the elements of SURF descriptors based on their entropies, divides SURF descriptorors into sub-descriptors, and sequentially and analytically matches them to each other is proposed.

Unbiased evaluation of keypoint detectors with respect to rotation invariance

TLDR
The most computationally complex detector, i.e. the SIFT performs best under rotation transformation of images, but the FAST and ORB detectors, while being less computationally demanding, perform almost equally well and can be viable choices in image processing tasks for mobile applications.

A fast, robust and low bit-rate representation for SIFT and SURF features

TLDR
This work investigates low bit-rate representations based on a binarisation of SIFT and SURF features and is able to reduce the descriptor length by a factor of 8–32 and lifts the curse of dimensionality.

KPB-SIFT: a compact local feature descriptor

TLDR
The produced KPB-SIFT descriptor is more compact as compared to the state-of-the-art, does not require pre-training step needed by PCA based descriptors, and shows superior advantages in terms of distinctiveness, invariance to scale, and tolerance of geometric distortions.

RSD-HoG: A New Image Descriptor

TLDR
A novel local image descriptor called RSD-HoG is proposed that performs better than many state of the art descriptors such as SIFT, GLOH, DAISY and PCA-SIFT and has rich, discriminative set of local information related to the curvature of the image surface.
...

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SURF: Speeded-Up Robust Features

In this document, the SURF detector-descriptor scheme used in the OpenSURF library is discussed in detail. First the algorithm is analysed from a theoretical standpoint to provide a detailed overview