| 1. |
- Lindeberg, Tony, 1964-, et al.
(author)
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Analysis of brain activation patterns using a 3-D scale-space primal sketch
- 1999
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In: Human Brain Mapping. - 1065-9471 .- 1097-0193. ; 7:3, s. 166-94
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Journal article (peer-reviewed)abstract
- A fundamental problem in brain imaging concerns how to define functional areas consisting of neurons that are activated together as populations. We propose that this issue can be ideally addressed by a computer vision tool referred to as the scale-space primal sketch. This concept has the attractive properties that it allows for automatic and simultaneous extraction of the spatial extent and the significance of regions with locally high activity. In addition, a hierarchical nested tree structure of activated regions and subregions is obtained. The subject in this article is to show how the scale-space primal sketch can be used for automatic determination of the spatial extent and the significance of rCBF changes. Experiments show the result of applying this approach to functional PET data, including a preliminary comparison with two more traditional clustering techniques. Compared to previous approaches, the method overcomes the limitations of performing the analysis at a single scale or assuming specific models of the data.
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| 2. |
- Bretzner, Lars, et al.
(author)
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Use your hand as a 3-D mouse or relative orientation from extended sequences of sparse point and line correspondances using the affine trifocal tensor
- 1998
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In: Computer Vision — ECCV'98. - : Springer Berlin/Heidelberg. ; , s. 141-157
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Conference paper (peer-reviewed)abstract
- This paper addresses the problem of computing three-dimensional structure and motion from an unknown rigid configuration of point and lines viewed by an affine projection model. An algebraic structure, analogous to the trilinear tensor for three perspective cameras, is defined for configurations of three centered affine cameras. This centered affine trifocal tensor contains 12 coefficients and involves linear relations between point correspondences and trilinear relations between line correspondences It is shown how the affine trifocal tensor relates to the perspective trilinear tensor, and how three-dimensional motion can be computed from this tensor in a straightforward manner. A factorization approach is also developed to handle point features and line features simultaneously in image sequences.This theory is applied to a specific problem of human-computer interaction of capturing three-dimensional rotations from gestures of a human hand. A qualitative model is presented, in which three fingers are represented by their position and orientation, and it is shown how three point correspondences (blobs at the finger tips) and three line correspondences (ridge features at the fingers) allow the affine trifocal tensor to be determined, from which the rotation is computed. Besides the obvious application, this test problem illustrates the usefulness of the affine trifocal tensor in a situation where sufficient information is not available to compute the perspective trilinear tensor, while the geometry requires point correspondences as well as line correspondences over at least three views.
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| 3. |
- Lindeberg, Tony, 1964-, et al.
(author)
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Förfarande och anordning för överföring av information genom rörelsedetektering, samt användning av anordningen : [Method and arrangement for controlling means for three-dimensional transfer of information by motion detection]
- 1998
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Patent (pop. science, debate, etc.)abstract
- The invention concerns a method and an arrangement for controlling means (24, 26), themselves controlled by processors, for three-dimensional transfer of information by motion detection using an image capturing device (20). Features of an object (10) are detected and transferred to line and point correspondences, which are used for controlling means (22, 26) to perform rotational and translational motion.
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| 4. |
- Eriksson, Patric, et al.
(author)
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A role for 'sensor simulation' and 'pre-emptive learning' in computer aided robotics
- 1995
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In: 26th International Symposium on Industrial Robots, Symposium Proceedings. - : Mechanical Engineering Publ.. - 1860580009 ; , s. 135-140
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Conference paper (peer-reviewed)abstract
- Sensor simulation in Computer Aided Robotics (CAR) can enhance the capabilities of such systems to enable off-line generation of programmes for sensor driven robots. However, such sensor simulation is not commonly supported in current computer aided robotic environments. A generic sensor object model for the simulation of sensors in graphical environments is described in this paper. Such a model can be used to simulate a variety of sensors, for example photoelectric, proximity and ultrasonic sensors. Tests results presented here show that this generic sensor model can be customised to emulate the characteristics of the real sensors. The preliminary findings from the first off-line trained mobile robot are presented. The results indicate that sensor simulation within CARs can be used to train robots to adapt to changing environments.
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| 5. |
- Almansa, Andrés, et al.
(author)
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Enhancement of Fingerprint Images by Shape-Adapted Scale-Space Operators
- 1996
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In: Gaussian Scale-Space Theory. Part I. - Dordrecht : Springer Science+Business Media B.V.. - 9789401588027 ; , s. 21-30
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Book chapter (peer-reviewed)abstract
- This work presents a novel technique for preprocessing fingerprint images. The method is based on the measurements of second moment descriptors and shape adaptation of scale-space operators with automatic scale selection (Lindeberg 1994). This procedure, which has been successfully used in the context of shape-from-texture and shape from disparity gradients, has several advantages when applied to fingerprint image enhancement, as observed by (Weickert 1995). For example, it is capable of joining interrupted ridges, and enforces continuity of their directional fields.In this work, these abovementioned general ideas are applied and extended in the following ways: Two methods for estimating local ridge width are explored and tuned to the problem of fingerprint enhancement. A ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model. This information is used for guiding a scale-selection mechanism, and 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. To a large extent, the scheme has the desirable property of joining interrupted lines without destroying essential singularities such as branching points. Thus, the result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a smoothed grey-level version of the input image.A detailed experimental evaluation is presented, including a comparison with other techniques. We propose that the techniques presented provide mechanisms of interest to developers of automatic fingerprint identification systems.
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| 6. |
- Lindeberg, Tony, 1964-
(author)
-
A scale selection principle for estimating image deformations
- 1998
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In: Image and Vision Computing. - 0262-8856 .- 1872-8138. ; 16, s. 961-977
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Journal article (peer-reviewed)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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| 7. |
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| 8. |
- Lindeberg, Tony, 1964-
(author)
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Edge detection and ridge detection with automatic scale selection
- 1998
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In: International Journal of Computer Vision. - : Kluwer Academic Publishers. - 0920-5691 .- 1573-1405. ; 30:2, s. 117-154
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Journal article (peer-reviewed)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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| 9. |
- Lindeberg, Tony
(author)
-
Edge detection and ridge detection with automatic scale selection
- 1996
-
In: Proc Computer Vision and Pattern Recognition (CPR’96). ; , s. 465-470
-
Conference paper (peer-reviewed)abstract
- 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 one-dimensional features, such as edges and ridges. A novel 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 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 γ-normalized 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 scale-space 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 scale-space ridge instead reflect the width of the ridge.
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| 10. |
- Lindeberg, Tony, 1964-
(author)
-
Linear spatio-temporal scale-space
- 1997
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In: Scale-Space Theory in Computer Vision. - Berlin, Heidelberg : Springer Berlin/Heidelberg. ; , s. 113-127
-
Conference paper (peer-reviewed)abstract
- This article shows how a linear scale-space formulation previously expressed for spatial domains extends to spatio-temporal data. Starting from the main assumptions that: (i) the scale-space should be generated by convolution with a semi-group of filter kernels and that (ii) local extrema must not be enhanced when the scale parameter increases, a complete taxonomy is given of the linear scale-space concepts that satisfy these conditions on spatial, temporal and spatio-temporal domains, including the cases with continuous as well as discrete data.Key aspects captured by this theory include that: (i) time-causal scale-space kernels must not extend into the future, (ii) filter shapes can be tuned from specific context information, permitting mechanisms such local shifting, shape adaptation and velocity adaptation, all expressed in terms of local diffusion operations.Receptive field profiles generated by the proposed theory show high qualitative similarities to receptive field profiles recorded from biological vision.
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