| 11. |
- Lindeberg, Tony, 1964-
(författare)
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Feature detection with automatic scale selection
- 1998
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Ingår i: International Journal of Computer Vision. ; 30:2, s. 79-116
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Tidskriftsartikel (refereegranskat)abstract
- 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. Whereas scale-space representation provides a well-founded framework for dealing with this issue by representing image structures at different scales, traditional scale-space theory does not address the problem of how to selectlocal appropriate scales for further analysis.This article proposes a systematic approach for dealing with this problem---a heuristic principle is presented stating that local extrema over scales of different combinations of gamma-normalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is proposed that this idea can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing to the local image structure.Support is given in terms of a general theoretical investigation of the behaviour of the scale selection method under rescalings of the input pattern and by experiments on real-world and synthetic data. Support is also given by a detailed analysis of how different types of feature detectors perform when integrated with a scale selection mechanism and then applied to characteristic model patterns. Specifically, it is described in detail how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation.
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| 12. |
- Lindeberg, Tony, 1964-
(författare)
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Principles for Automatic Scale Selection
- 1999
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Ingår i: Handbook on Computer Vision and Applications. - : Academic Press. ; , s. 239-274
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- 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 real-world signals, then a multi-scale representation of data is of crucial importance. Whereas conventional scale-space theory provides a well-founded framework for dealing with image structures at different scales, this theory does not directly address the problem of how to selectappropriate scales for further analysis. This article outlines a systematic methodology of how mechanisms for automatic scale selection can be formulated in the problem domains of feature detection and image matching (flow 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 $\gamma$-normalized derivatives arises by necessity given the requirement that the scale selection mechanism should commute with rescalings of the image pattern. Specifically, it is worked out in detail how feature detection algorithms with automatic scale selection can be formulated for the problems of edge detection, blob detection, junction detection, ridge detection and frequency estimation. A general property of this scheme is that the selected scale levels reflect the size of the image structures.When estimating image deformations, such as in image matching and optic flow computations, scale levels with associated deformation estimates can be selected from the scales at which normalized measures of uncertainty assume local minima with respect to scales. It is shown how an integrated scale selection and flow estimation algorithm has the qualitative properties of leading to the selection of coarser scales for larger size image structures and increasing noise level, whereas it leads to the selection of finer scales in the neighbourhood of flow field discontinuities.
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| 13. |
- Lindeberg, Tony, 1964-, et al.
(författare)
-
Segmentation and classification of edges using minimum description length approximation and complementary junction cues
- 1995
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Ingår i: Theory and Applications of Image Analysis II. - : World Scientific.
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Bokkapitel (refereegranskat)abstract
- 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 multi-scale pre-processing 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.
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| 14. |
- Lindeberg, Tony, 1964-, et al.
(författare)
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Segmentation and classification of edges using minimum description length approximation and complementary junction cues
- 1997
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Ingår i: Computer Vision and Image Understanding. - : Elsevier. - 1077-3142 .- 1090-235X. ; 67:1, s. 88-98
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 15. |
- Lindeberg, Tony, 1964-
(författare)
-
A scale selection principle for estimating image deformations
- 1998
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Ingår i: Image and Vision Computing. - 0262-8856 .- 1872-8138. ; 16, s. 961-977
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Tidskriftsartikel (refereegranskat)abstract
- A basic functionality of a vision system concerns the ability to compute deformation fields between different images of the same physical structure. This article advocates the need for incorporating explicit mechanisms for scale selection in this context, in algorithms for computing descriptors such as optic flow and for performing stereo matching. A basic reason why such a mechanism is essential is the fact that in a coarse-to-fine propagation of disparity or flow information, it is not necessarily the case that the most accurate estimates are obtained at the finest scales. The existence of interfering structures at fine scales may make it impossible to accurately match the image data at fine scales. selecting deformation estimates from the scales that minimize the (suitably normalized) uncertainty over scales. A specific implementation of this idea is presented for a region based differential flow estimation scheme. It is shown that the integrated scale selection and flow estimation algorithm has the qualitative properties of leading to the selection of coarser scales for larger size image structures and increasing noise level, whereas it leads to the selection of finer scales in the neighbourhood of flow field discontinuities
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| 16. |
- Lindeberg, Tony, 1964-
(författare)
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Edge detection and ridge detection with automatic scale selection
- 1998
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Ingår i: International Journal of Computer Vision. - : Kluwer Academic Publishers. - 0920-5691 .- 1573-1405. ; 30:2, s. 117-154
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Tidskriftsartikel (refereegranskat)abstract
- When computing descriptors of image data, the type of information that can be extracted may be strongly dependent on the scales at which the image operators are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of scale levels when detecting one-dimensional image features, such as edges and ridges.A concept of a scale-space edge is introduced, defined as a connected set of points in scale-space 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 consequence 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, the gradient magnitude and a differential expression derived from the third-order derivative in the gradient direction. For a certain way of normalizing these differential descriptors, by expressing them in terms of so-called gamma-normalized derivatives, an immediate consequence of this definition is that the edge detector will adapt its scale levels to the local image structure. Specifically, sharp edges will be detected at fine scales so as to reduce the shape distortions due to scale-space smoothing, whereas sufficiently coarse scales will be selected at diffuse edges, such that an edge model is a valid abstraction of the intensity profile across the edge.Since the scale-space edge is defined from the intersection of two zero-crossing surfaces in scale-space, the edges will by definition form closed curves. This simplifies selection of salient edges, and a novel significance measure is proposed, by integrating the edge strength along the edge. Moreover, the scale information associated with each edge provides useful clues to the physical nature of the edge.With just slight modifications, similar ideas can be used for formulating ridge detectors with automatic selection, having the characteristic property that the selected scales on a scale-space ridge instead reflect the width of the ridge.It is shown how the methodology can be implemented in terms of straightforward visual front-end operations, and the validity of the approach is supported by theoretical analysis as well as experiments on real-world and synthetic data.
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| 17. |
- Lindeberg, Tony, 1964-
(författare)
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On the axiomatic foundations of linear scale-space : Combining semi-group structure with causality vs. scale invariance
- 1996
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Ingår i: Gaussian Scale-Space Theory. - : Kluwer Academic Publishers.
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Bokkapitel (refereegranskat)abstract
- The notion of multi-scale representation is essential to many aspects of early visual processing. This article deals with the axiomatic formulation of the special type of multi-scale representation known as scale-space representation. Specifically, this work is concerned with the problem of how different choices of basic assumptions (scale-space axioms) restrict the class of permissible smoothing operations.A scale-space formulation previously expressed for discrete signals is adapted to the continuous domain. The basic assumptions are that the scale-space family should be generated by convolution with a one-parameter family of rotationally symmetric smoothing kernels that satisfy a semi-group structure and obey a causality condition expressed as a non-enhancement requirement of local extrema. Under these assumptions, it is shown that the smoothing kernel is uniquely determined to be a Gaussian.Relations between this scale scale-space formulation and recent formulations based on scale invariance are explained in detail. Connections are also pointed out to approaches based on non-uniform smoothing.
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| 18. |
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| 19. |
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| 20. |
- Lindeberg, Tony, Professor, 1964-
(författare)
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Updates to : Scale-Space Theory in Computer Vision
- 1996
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This document contains corrections and additional remarks to:Scale-Space Theory in Computer Vision by Tony Lindeberg, published by Kluwer Academic Publishers, Dordrecht, The Netherlands, 1993.Last update: October 4, 1996
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