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48
An affine invariant interest point detector
 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 ..."
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Cited by 1467 (55 self)
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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 affineadapted Harris detector determines the location of interest points. A multiscale 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.
Spacetime Interest Points
 IN ICCV
, 2003
"... Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we propose to extend the notion of spatial interest points into the spatiotemporal domain and show how the resulting features often reflect interesting events that can be use ..."
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Cited by 819 (21 self)
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Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we propose to extend the notion of spatial interest points into the spatiotemporal domain and show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for its interpretation.. To detect
Feature detection with automatic scale selection
 International Journal of Computer Vision
, 1998
"... The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works ..."
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Cited by 723 (34 self)
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The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a socalled scalespace representation. Traditional scalespace theory building on this work, however, does not address the problem of how to select local appropriate scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is proposed for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of γnormalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is shown how this idea can be used as a major mechanism in algorithms for automatic scale selection, which
Principles for automatic scale selection
 Handbook on Computer Vision and Applications
, 1999
"... 1Abstract: An inherent property of objects in the world is that they only exist as meaningful entities over certain ranges of scale. If one aims at describing the structure of unknown realworld signals, then a multiscale representation of data is of crucial importance. Whereas conventional scales ..."
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Cited by 36 (3 self)
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1Abstract: An inherent property of objects in the world is that they only exist as meaningful entities over certain ranges of scale. If one aims at describing the structure of unknown realworld signals, then a multiscale representation of data is of crucial importance. Whereas conventional scalespace theory provides a wellfounded framework for dealing with image structures at dierent scales, this theory does not directly address the problem of how to select appropriate scales for further analysis. This chapter outlines a systematic methodology of how mechanisms for automatic scale selection can be formulated in the problem domains of feature detection and image matching (
ow estimation), respectively. For feature detectors expressed in terms of Gaussian derivatives, hypotheses about interesting scale levels can be generated from scales at which normalized measures of feature strength assume local maxima with respect to scale. It is shown how the notion of normalized derivatives arises by necessity given the requirement that the scale selection mechanism should
RealTime Scale Selection in Hybrid MultiScale Representations
, 2003
"... Local scale information extracted from visual data in a bottom up manner constitutes an important cue for a large number of visual tasks. This article presents a framework for how the computation of such scale descriptors can be performed in real time on a standard computer. ..."
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Cited by 29 (6 self)
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Local scale information extracted from visual data in a bottom up manner constitutes an important cue for a large number of visual tasks. This article presents a framework for how the computation of such scale descriptors can be performed in real time on a standard computer.
Local Features for Enhancement and Minutiae Extraction in Fingerprints
, 2008
"... Accurate fingerprint recognition presupposes robust feature extraction which is often hampered by noisy input data. We suggest common techniques for both enhancement and minutiae extraction, employing symmetry features. For enhancement, a Laplacianlike image pyramid is used to decompose the origina ..."
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Cited by 16 (0 self)
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Accurate fingerprint recognition presupposes robust feature extraction which is often hampered by noisy input data. We suggest common techniques for both enhancement and minutiae extraction, employing symmetry features. For enhancement, a Laplacianlike image pyramid is used to decompose the original fingerprint into subbands corresponding to different spatial scales. In a further step, contextual smoothing is performed on these pyramid levels, where the corresponding filtering directions stem from the frequencyadapted structure tensor (linear symmetry features). For minutiae extraction, parabolic symmetry is added to the local fingerprint model which allows to accurately detect the position and direction of a minutia simultaneously. Our experiments support the view that using the suggested parabolic symmetry features, the extraction of which does not require explicit thinning or other morphological operations, constitute a robust alternative to conventional minutiae extraction. All necessary image processing is done in the spatial domain using 1D filters only, avoiding block artifacts that reduce the biometric information. We present comparisons to other studies on enhancement in matching tasks employing the open source matcher from NIST, FIS2. Furthermore, we compare the proposed minutiae extraction method with the corresponding method from the NIST package, mindtct. A top five commercial matcher from FVC2006 is used in enhancement quantification as well. The matching error is lowered significantly when plugging in the suggested methods. The FVC2004 fingerprint database, notable for its exceptionally lowquality fingerprints, is used for all experiments.
Fast anisotropic Gauss filtering
 IEEE Transaction on Image Processing
"... Abstract. We derive the decomposition of the anisotropic Gaussian in a one dimensional Gauss filter in the xdirection followed by a one dimensional filter in a nonorthogonal direction ϕ. So also the anisotropic Gaussian can be decomposed by dimension. This appears to be extremely efficient from a ..."
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Cited by 12 (1 self)
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Abstract. We derive the decomposition of the anisotropic Gaussian in a one dimensional Gauss filter in the xdirection followed by a one dimensional filter in a nonorthogonal direction ϕ. So also the anisotropic Gaussian can be decomposed by dimension. This appears to be extremely efficient from a computing perspective. An implementation scheme for normal convolution and for recursive filtering is proposed. Also directed derivative filters are demonstrated. For the recursive implementation, filtering an 512 × 512 image is performed within 65 msec, independent of the standard deviations and orientation of the filter. Accuracy of the filters is still reasonable when compared to truncation error or recursive approximation error. The anisotropic Gaussian filtering method allows fast calculation of edge and ridge maps, with high spatial and angular accuracy. For tracking applications, the normal anisotropic convolution scheme is more advantageous, with applications in the detection of dashed lines in engineering drawings. The recursive implementation is more attractive in feature detection applications, for instance in affine invariant edge and ridge detection in computer vision. The proposed computational filtering method enables the practical applicability of orientation scalespace analysis.
VelocityAdaptation of SpatioTemporal Receptive Fields for Direct Recognition of Activities: An experimental study
 IVC
, 2002
"... This article presents an experimental study of the influence of velocity adaptation when recognizing spatiotemporal patterns using a histogrambased statistical framework. The basic idea consists of adapting the shapes of the filter kernels to the local direction of motion, so as to allow the compu ..."
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Cited by 11 (9 self)
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This article presents an experimental study of the influence of velocity adaptation when recognizing spatiotemporal patterns using a histogrambased statistical framework. The basic idea consists of adapting the shapes of the filter kernels to the local direction of motion, so as to allow the computation of image descriptors that are invariant to the relative motion in the image plane between the camera and the objects or events that are studied. Based on a framework of recursive spatiotemporal scalespace, we first outline how a straightforward mechanism for local velocity adaptation can be expressed. Then, for a test problem of recognizing activities, we present an experimental evaluation, which shows the advantages of using velocityadapted spatiotemporal receptive fields, compared to directional derivatives or regular partial derivatives for which the filter kernels have not been adapted to the local image motion.
Multiscale segmentation of the aorta in 3D ultrasound images
 in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’03
, 2003
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Interest point detection and scale selection in spacetime
 of Lecture Notes in Computer Science
, 2003
"... Abstract. Several types of interest point detectors have been proposed for spatial images. This paper investigates how this notion can be generalised to the detection of interesting events in spacetime data. Moreover, we develop a mechanism for spatiotemporal scale selection and detect events at s ..."
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Cited by 8 (1 self)
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Abstract. Several types of interest point detectors have been proposed for spatial images. This paper investigates how this notion can be generalised to the detection of interesting events in spacetime data. Moreover, we develop a mechanism for spatiotemporal scale selection and detect events at scales corresponding to their extent in both space and time. To detect spatiotemporal events, we build on the idea of the Harris and Förstner interest point operators and detect regions in spacetime where the image structures have significant local variations in both space and time. In this way, events that correspond to curved spacetime structures are emphasised, while structures with locally constant motion are disregarded. To construct this operator, we start from a multiscale windowed second moment matrix in spacetime, and combine the determinant and the trace in a similar way as for the spatial Harris operator. All spacetime maxima of this operator are then adapted to characteristic scales by maximising a scalenormalised spacetime Laplacian operator over both spatial scales and temporal scales. The motivation for performing temporal scale selection as a complement to previous approaches of spatial scale selection is to be able to robustly capture spatiotemporal events of different temporal extent. It is shown that the resulting approach is truly scale invariant with respect to both spatial scales and temporal scales. The proposed concept is tested on synthetic and real image sequences. It is shown that the operator responds to distinct and stable points in spacetime that often correspond to interesting events. The potential applications of the method are discussed. 1