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12
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 488 (26 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
Edge Detection and Ridge Detection with Automatic Scale Selection
 CVPR'96
, 1996
"... When extracting features from image data, the type of information that can be extracted may be strongly dependent on the scales at which the feature detectors are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of ..."
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Cited by 247 (20 self)
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When extracting features from image data, the type of information that can be extracted may be strongly dependent on the scales at which the feature detectors are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of scale levels when detecting onedimensional features, such as edges and ridges. Anovel concept of a scalespace edge is introduced, defined as a connected set of points in scalespace at which: (i) the gradient magnitude assumes a local maximum in the gradient direction, and (ii) a normalized measure of the strength of the edge response is locally maximal over scales. An important property of this definition is that it allows the scale levels to vary along the edge. Two specific measures of edge strength are analysed in detail. It is shown that by expressing these in terms of γnormalized derivatives, an immediate consequence of this definition is that fine scales are selected for sharp edges (so as to reduce the shape distortions due to scalespace smoothing), whereas coarse scales are selected for diffuse edges, such that an edge model constitutes a valid abstraction of the intensity profile across the edge. With slight modifications, this idea can be used for formulating a ridge detector with automatic scale selection, having the characteristic property that the selected scales on a scalespace ridge instead reflect the width of the ridge.
Detecting Salient BlobLike Image Structures with a ScaleSpace Primal Sketch: A Method for FocusofAttention
 INT. J. COMP. VISION
, 1993
"... This article presents: (i) a multiscale representation of greylevel shape called the scalespace primal sketch, which makes explicit both features in scalespace and the relations between structures at different scales, (ii) a methodology for extracting significant bloblike image structures from ..."
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Cited by 151 (14 self)
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This article presents: (i) a multiscale representation of greylevel shape called the scalespace primal sketch, which makes explicit both features in scalespace and the relations between structures at different scales, (ii) a methodology for extracting significant bloblike image structures from this representations, and (iii) applications to edge detection, histogram analysis, and junction classification demonstrating how the proposed method can be used for guiding later stage visual processes. The representation gives a qualitative description of image structure, which allows for detection of stable scales and associated regions of interest in a solely bottomup datadriven way. In other words, it generates coarse segmentation cues, and can hence be seen as preceding further processing, which can then be properly tuned. It is argued that once such information is available, many other processing tasks can become much simpler. Experiments on real imagery demonstrate that the proposed theory gives intuitive results.
Shapeadapted smoothing in estimation of 3D shape cues from affine distortions of local 2D brightness structure
, 2001
"... This article describes a method for reducing the shape distortions due to scalespace smoothing that arise in the computation of 3D shape cues using operators (derivatives) de ned from scalespace representation. More precisely, we are concerned with a general class of methods for deriving 3D shap ..."
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Cited by 52 (3 self)
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This article describes a method for reducing the shape distortions due to scalespace smoothing that arise in the computation of 3D shape cues using operators (derivatives) de ned from scalespace representation. More precisely, we are concerned with a general class of methods for deriving 3D shape cues from 2D image data based on the estimation of locally linearized deformations of brightness patterns. This class
Fingerprint Enhancement by Shape Adaptation of ScaleSpace Operators with Automatic Scale Selection
"... This work presents two mechanisms for processing fingerprint images; shapeadapted smoothing based on second moment descriptors and automatic scale selection based on normalized derivatives. The shape adaptation procedure adapts the smoothing operation to the local ridge structures, which allows int ..."
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Cited by 38 (9 self)
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This work presents two mechanisms for processing fingerprint images; shapeadapted smoothing based on second moment descriptors and automatic scale selection based on normalized derivatives. The shape adaptation procedure adapts the smoothing operation to the local ridge structures, which allows interrupted ridges to be joined without destroying essential singularities such as branching points and enforces continuity of their directional fields. The scale selection procedure estimates local ridge width and adapts the amount of smoothing to the local amount of noise. In addition, a ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model, and is used for spreading the results of shape adaptation into noisy areas. The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. The result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a smoothed greylevel version of the input image. We propose that these general techniques should be of interest to developers of automatic fingerprint identification
Discrete derivative approximations with scalespace properties: A basis for lowlevel feature extraction
 J. Math. Imaging Vision
, 1993
"... It is developed how discrete derivative approximations can be de ned so that scalespace properties hold exactly also in the discrete domain. Starting from a set of natural requirements on the rst processing stages of a visual system, the visual front end, an axiomatic derivation is given of how amu ..."
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Cited by 28 (12 self)
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It is developed how discrete derivative approximations can be de ned so that scalespace properties hold exactly also in the discrete domain. Starting from a set of natural requirements on the rst processing stages of a visual system, the visual front end, an axiomatic derivation is given of how amultiscale representation of derivative approximations can be constructed from a discrete signal, so that it possesses an algebraic structure similar to that possessed by the derivatives of the traditional scalespace representation in the continuous domain. A family of kernels is derived which constitute discrete analogues to the continuous Gaussian derivatives. The representation has theoretical advantages to other discretizations of the scalespace theory in the sense that operators which commute before discretization commute after discretization. Some computational implications of this are that derivativeapproximations can be computed directly from smoothed data, and that this will give exactly the same result as convolution with the corresponding derivative approximation kernel. Moreover, a number of normalization conditions are automatically satis ed. The proposed methodology leads to a conceptually very simple scheme of computations for multiscale lowlevel feature extraction, consisting of four basic steps � (i) large support convolution smoothing, (ii) small support di erence computations, (iii) point operations for computing di erential geometric entities, and (iv) nearest neighbour operations for feature detection. Applications are given demonstrating how the proposed scheme can be used for edge detection and junction detection based on derivatives up to order three.
Feature Tracking with Automatic Selection of Spatial Scales
 Computer Vision and Image Understanding
, 1996
"... When observing a dynamic world, the size of image structures may vary over time. ..."
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Cited by 24 (8 self)
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When observing a dynamic world, the size of image structures may vary over time.
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 17 (3 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.
Segmentation and Classification of Edges Using Minimum Description Length Approximation and Complementary Junction Cues
, 1996
"... This article presents a method for segmenting and classifying edges using minimum description length (MDL) approximation with automatically generated break points. A scheme is proposed where junction candidates are first detected in a multiscale preprocessing step, which generates junction candidat ..."
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Cited by 11 (1 self)
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This article presents a method for segmenting and classifying edges using minimum description length (MDL) approximation with automatically generated break points. A scheme is proposed where junction candidates are first detected in a multiscale preprocessing step, which generates junction candidates with associated regions of interest. These junction features are matched to edges based on spatial coincidence. For each matched pair, a tentative break point is introduced at the edge point closest to the junction. Finally, these feature combinations serve as input for an MDL approximation method which tests the validity of the break point hypotheses and classifies the resulting edge segments as either "straight " or "curved". Experiments on real world image data demonstrate the viability of the approach.
On Automatic Selection of Temporal Scales in TimeCausal ScaleSpace
 of Lecture Notes in Computer Science
, 1997
"... This paper outlines a general framework for automatic selection in temporal scalespace representations, and shows how the suggested theory applies to motion detection and motion estimation. ..."
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Cited by 9 (7 self)
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This paper outlines a general framework for automatic selection in temporal scalespace representations, and shows how the suggested theory applies to motion detection and motion estimation.