## Surf: Speeded up robust features (2006)

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Venue: | In ECCV |

Citations: | 430 - 10 self |

### BibTeX

@INPROCEEDINGS{Bay06surf:speeded,

author = {Herbert Bay and Tinne Tuytelaars and Luc Van Gool},

title = {Surf: Speeded up robust features},

booktitle = {In ECCV},

year = {2006},

pages = {404--417}

}

### Years of Citing Articles

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### Abstract

Abstract. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF’s strong performance. 1

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Citation Context ...ations. Skew, anisotropic scaling, and perspective effects are assumed to be second-order effects, that are covered to some degree by the overall robustness of the descriptor. As also claimed by Lowe =-=[2]-=-, the additional complexity of full affine-invariant features often has a negative impact on their robustness and does not pay off, unless really large viewpoint changes are to be expected. In some ca... |

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1154 | Performance evaluation of local descriptors
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Citation Context ...res [21], and descriptors representing the distribution of smaller-scale features within the interest point neighbourhood. The latter, introduced by Lowe [2], have been shown to outperform the others =-=[7]-=-. This can be explained by the fact that they capture a substantial amount of information about the spatial intensity patterns, while at the same time being robust to small deformations or localisatio... |

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660 |
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247 | Reliable feature matching across widely separated views
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Citation Context ... at a low cost in terms of lost accuracy. Feature Descriptors An even larger variety of feature descriptors has been proposed, like Gaussian derivatives [16], moment invariants [17], complex features =-=[18, 19]-=-, steerable filters [20], phase-based local features [21], and descriptors representing the distribution of smaller-scale features within the interest point neighbourhood. The latter, introduced by Lo... |

236 | comparison of affine region detectors
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Citation Context ...) (see Fig. 6, discussed below). The descriptor evaluations are shown for all sequences except the Bark sequence (see Fig. 4 and 7). For the detectors, we use the repeatability score, as described in =-=[9]-=-. This indicates how many of the detected interest points are found in both images, relative to the lowest total number of interest points found (where only the part of the image that is visible in bo... |

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Citation Context ...aximum suppression in a 3 × 3 × 3 neighbourhood is applied. The maxima of the determinant of the Hessian matrix are then interpolated in scale and image space with the method proposed by Brown et al. =-=[27]-=-. Scale space interpolation is especially important in our case, as the difference in scale betweens6 H. Bay, T. Tuytelaars, and L. Van Gool Fig. 2. Left: Detected interest points for a Sunflower fiel... |

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Citation Context ... An even larger variety of feature descriptors has been proposed, like Gaussian derivatives [16], moment invariants [17], complex features [18, 19], steerable filters [20], phase-based local features =-=[21]-=-, and descriptors representing the distribution of smaller-scale features within the interest point neighbourhood. The latter, introduced by Lowe [2], have been shown to outperform the others [7]. Thi... |

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Citation Context ...the DoG can bring speed at a low cost in terms of lost accuracy. Feature Descriptors An even larger variety of feature descriptors has been proposed, like Gaussian derivatives [16], moment invariants =-=[17]-=-, complex features [18, 19], steerable filters [20], phase-based local features [21], and descriptors representing the distribution of smaller-scale features within the interest point neighbourhood. T... |

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Citation Context ...ng images are sub-sampled. Also, the property that no new structures can appear while going to lower resolutions may have been proven in the 1D case, but is known to not apply in the relevant 2D case =-=[25]-=-. Hence, the importance of the Gaussian seems to have been somewhat overrated in this regard, and here we test a simpler alternative. As Gaussian filters are non-ideal in any case, and given Lowe’s su... |

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Citation Context ...tives scale accordingly. Thus, for example, our 27 × 27 filter corresponds to σ =3× 1.2 =3.6 =s. Furthermore, as the Frobenius norm remains constant for our filters, they are already scale normalised =-=[26]-=-. In order to localise interest points in the image and over scales, a nonmaximum suppression in a 3 × 3 × 3 neighbourhood is applied. The maxima of the determinant of the Hessian matrix are then inte... |

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