Reliable feature matching across widely separated views. In: (2000)

by A Baumberg
Venue:IEEE Conference on Computer Vision and Pattern Recognition,
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Distinctive Image Features from Scale-Invariant Keypoints

by David G. Lowe , 2003
"... This paper presents a method for extracting distinctive invariant features from images, which can be used to perform reliable matching between different images of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substa ..."
Abstract - Cited by 8955 (21 self) - Add to MetaCart
This paper presents a method for extracting distinctive invariant features from images, which can be used to perform reliable matching between different images of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substantial range of affine distortion, addition of noise, change in 3D viewpoint, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through leastsquares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

A PERFORMANCE EVALUATION OF LOCAL DESCRIPTORS

by Krystian Mikolajczyk, Cordelia Schmid , 2005
"... In this paper we compare the performance of descriptors computed for local interest regions, as for example extracted by the Harris-Affine detector [32]. Many different descriptors have been proposed in the literature. However, it is unclear which descriptors are more appropriate and how their perfo ..."
Abstract - Cited by 1783 (51 self) - Add to MetaCart
In this paper we compare the performance of descriptors computed for local interest regions, as for example extracted by the Harris-Affine detector [32]. Many different descriptors have been proposed in the literature. However, it is unclear which descriptors are more appropriate and how their performance depends on the interest region detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector. Our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations. We compare shape context [3], steerable filters [12], PCA-SIFT [19], differential invariants [20], spin images [21], SIFT [26], complex filters [37], moment invariants [43], and cross-correlation for different types of interest regions. We also propose an extension of the SIFT descriptor, and show that it outperforms the original method. Furthermore, we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT based descriptors perform best. Moments and steerable filters show the best performance among the low dimensional descriptors.
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Video google: A text retrieval approach to object matching in videos

by Josef Sivic, Andrew Zisserman - In ICCV , 2003
"... We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, ill ..."
Abstract - Cited by 1636 (42 self) - Add to MetaCart
We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieval is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching on two full length feature films. 1.
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An affine invariant interest point detector

by Krystian Mikolajczyk, Cordelia Schmid - In Proceedings of the 7th European Conference on Computer Vision , 2002
"... Abstract. This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape of ..."
Abstract - Cited by 1467 (55 self) - Add to MetaCart
Abstract. This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape of the neighbourhood of an interest point. Our approach allows to solve for these problems simultaneously. It is based on three key ideas: 1) The second moment matrix computed in a point can be used to normalize a region in an affine invariant way (skew and stretch). 2) The scale of the local structure is indicated by local extrema of normalized derivatives over scale. 3) An affine-adapted Harris detector determines the location of interest points. A multi-scale version of this detector is used for initialization. An iterative algorithm then modifies location, scale and neighbourhood of each point and converges to affine invariant points. For matching and recognition, the image is characterized by a set of affine invariant points; the affine transformation associated with each point allows the computation of an affine invariant descriptor which is also invariant to affine illumination changes. A quantitative comparison of our detector with existing ones shows a significant improvement in the presence of large affine deformations. Experimental results for wide baseline matching show an excellent performance in the presence of large perspective transformations including significant scale changes. Results for recognition are very good for a database with more than 5000 images.
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Scalable Recognition with a Vocabulary Tree

by David Nistér, Henrik Stewénius - IN CVPR , 2006
"... A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD's. The scheme ..."
Abstract - Cited by 1051 (0 self) - Add to MetaCart
A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD's. The scheme

Robust wide baseline stereo from maximally stable extremal regions

by J. Matas, O. Chum, M. Urban, T. Pajdla - In Proc. BMVC , 2002
"... The wide-baseline stereo problem, i.e. the problem of establishing correspon-dences between a pair of images taken from different viewpoints is studied. A new set of image elements that are put into correspondence, the so called extremal regions, is introduced. Extremal regions possess highly de-sir ..."
Abstract - Cited by 1016 (35 self) - Add to MetaCart
The wide-baseline stereo problem, i.e. the problem of establishing correspon-dences between a pair of images taken from different viewpoints is studied. A new set of image elements that are put into correspondence, the so called extremal regions, is introduced. Extremal regions possess highly de-sirable properties: the set is closed under 1. continuous (and thus projective) transformation of image coordinates and 2. monotonic transformation of im-age intensities. An efficient (near linear complexity) and practically fast de-tection algorithm (near frame rate) is presented for an affinely-invariant stable subset of extremal regions, the maximally stable extremal regions (MSER). A new robust similarity measure for establishing tentative correspon-dences is proposed. The robustness ensures that invariants from multiple measurement regions (regions obtained by invariant constructions from ex-tremal regions), some that are significantly larger (and hence discriminative) than the MSERs, may be used to establish tentative correspondences. The high utility of MSERs, multiple measurement regions and the robust metric is demonstrated in wide-baseline experiments on image pairs from both indoor and outdoor scenes. Significant change of scale (3.5×), illumi-nation conditions, out-of-plane rotation, occlusion, locally anisotropic scale change and 3D translation of the viewpoint are all present in the test prob-lems. Good estimates of epipolar geometry (average distance from corre-sponding points to the epipolar line below 0.09 of the inter-pixel distance) are obtained. 1
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SURF: Speeded Up Robust Features

by Herbert Bay, Tinne Tuytelaars, Luc Van Gool - ECCV
"... Abstract. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Ro-bust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be comp ..."
Abstract - Cited by 897 (12 self) - Add to MetaCart
Abstract. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Ro-bust 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 descrip-tors (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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Indexing based on scale invariant interest points

by Krystian Mikolajczyk, Cordelia Schmid - In Proceedings of the 8th International Conference on Computer Vision , 2001
"... This paper presents a new method for detecting scale invariant interest points. The method is based on two recent results on scale space: 1) Interest points can be adapted to scale and give repeatable results (geometrically stable). 2) Local extrema over scale of normalized derivatives indicate the ..."
Abstract - Cited by 409 (32 self) - Add to MetaCart
This paper presents a new method for detecting scale invariant interest points. The method is based on two recent results on scale space: 1) Interest points can be adapted to scale and give repeatable results (geometrically stable). 2) Local extrema over scale of normalized derivatives indicate the presence of characteristic local structures. Our method first computes a multi-scale representation for the Harris interest point detector. We then select points at which a local measure (the Laplacian) is maximal over scales. This allows a selection of distinctive points for which the characteristic scale is known. These points are invariant to scale, rotation and translation as well as robust to illumination changes and limited changes of viewpoint. For indexing, the image is characterized by a set of scale invariant points; the scale associated with each point allows the computation of a scale invariant descriptor. Our descriptors are, in addition, invariant to image rotation, to affine illumination changes and robust to small perspective deformations. Experimental results for indexing show an excellent performance up to a scale factor of 4 for a database with more than 5000 images. 1
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A comparison of affine region detectors

by K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, L. Van Gool - International Journal of Computer Vision , 2005
"... The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris [24, 34] and Hessian points [24 ..."
Abstract - Cited by 364 (19 self) - Add to MetaCart
The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris [24, 34] and Hessian points [24], as proposed by Mikolajczyk and Schmid and by Schaffalitzky and Zisserman; a detector of ‘maximally stable extremal regions’, proposed by Matas et al. [21]; an edge-based region detector [45] and a detector based on intensity extrema [47], proposed by Tuytelaars and Van Gool; and a detector of ‘salient regions’, proposed by Kadir, Zisserman and Brady [12]. The performance is measured against changes in viewpoint, scale, illumination, defocus and image compression. The objective of this paper is also to establish a reference test set of images and performance software, so that future detectors can be evaluated in the same framework. 1
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Speeded-Up Robust Features (SURF)

by Herbert Bay , Andreas Ess , Tinne Tuytelaars , Luc Van Gool , B K. U. Leuven , 2008
"... This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faste ..."
Abstract - Cited by 313 (5 self) - Add to MetaCart
This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF 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 (specifically, 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 encompasses a detailed description of the detector and descriptor and then explores the effect of the most important parameters. We conclude the article with SURF’s application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF’s usefulness in a broad range of topics in computer vision.