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Procedings of the British Machine Vision Conference 2003, 2003
Artists pictures rarely have photo-realistic detail. Tools to create pictures from digital photographs might, therefore, include methods for removing detail. These tools such as Gaussian and anisotropic diffusion filters and connected-set morphological filters (sieves) remove detail whilst maintaining scale-space causality, in other words new detail is
J. Andrew Bangham +2 more
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Artists pictures rarely have photo-realistic detail. Tools to create pictures from digital photographs might, therefore, include methods for removing detail. These tools such as Gaussian and anisotropic diffusion filters and connected-set morphological filters (sieves) remove detail whilst maintaining scale-space causality, in other words new detail is
J. Andrew Bangham +2 more
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2019
We introduce a novel scale-space concept that is inspired by inpainting-based lossy image compression and the recent denoising by inpainting method of Adam et al. (2017). In the discrete setting, the main idea behind these so-called sparsification scale-spaces is as follows: Starting with the original image, one subsequently removes a pixel until a ...
Cárdenas, Marcelo +2 more
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We introduce a novel scale-space concept that is inspired by inpainting-based lossy image compression and the recent denoising by inpainting method of Adam et al. (2017). In the discrete setting, the main idea behind these so-called sparsification scale-spaces is as follows: Starting with the original image, one subsequently removes a pixel until a ...
Cárdenas, Marcelo +2 more
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2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008
Local visual homing is the process of determining the direction of movement required to return an agent to a goal location by comparing the current image with an image taken at the goal, known as the snapshot image. One way of accomplishing visual homing is by computing the correspondences between features and then analyzing the resulting flow field to
David Churchill, Andrew Vardy
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Local visual homing is the process of determining the direction of movement required to return an agent to a goal location by comparing the current image with an image taken at the goal, known as the snapshot image. One way of accomplishing visual homing is by computing the correspondences between features and then analyzing the resulting flow field to
David Churchill, Andrew Vardy
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Image and Vision Computing, 1997
It is argued that image measurements should satisfy two requirements of physical plausibility: the measurements are of non-zero scale and non-zero imprecision; and two required invariances, nothing is lost by expanding the image and nothing is lost by increasing the contrast of the image.
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It is argued that image measurements should satisfy two requirements of physical plausibility: the measurements are of non-zero scale and non-zero imprecision; and two required invariances, nothing is lost by expanding the image and nothing is lost by increasing the contrast of the image.
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2005
In this paper we extend the notion of Poisson scale-space. We propose a generalisation inspired by the linear parabolic pseudodifferential operator $\sqrt{-\Delta+m^2}-m$, 0≤m, connected with models of relativistic kinetic energy from quantum mechanics.
Bernhard Burgeth +2 more
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In this paper we extend the notion of Poisson scale-space. We propose a generalisation inspired by the linear parabolic pseudodifferential operator $\sqrt{-\Delta+m^2}-m$, 0≤m, connected with models of relativistic kinetic energy from quantum mechanics.
Bernhard Burgeth +2 more
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A Dynamic Scale–Space Paradigm
Journal of Mathematical Imaging and Vision, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alfons H. Salden +2 more
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1987
The extrema in a signal and its first few derivatives provide a useful general-purpose qualitative description for many kinds of signals. A fundamental problem in computing such descriptions is scale: a derivative must be taken over some neighborhood, but there is seldom a principled basis for choosing its size.
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The extrema in a signal and its first few derivatives provide a useful general-purpose qualitative description for many kinds of signals. A fundamental problem in computing such descriptions is scale: a derivative must be taken over some neighborhood, but there is seldom a principled basis for choosing its size.
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From Gaussian scale-space to B-spline scale-space
1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999The Gaussian kernel has long been used in the classical multiscale analysis. The purpose of the paper is to propose the uniform B-spline as an alternative for the visual modeling. A general framework for various scale-space representations is formulated using the B-spline approach.
Wang, Yu-Ping, Lee, S.L.
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IEEE Winter Conference on Applications of Computer Vision, 2014
The state-of-the-art SIFT flow has been widely adopted for the general image matching task, especially in dealing with image pairs from similar scenes but with different object configurations. However, the way in which the dense SIFT features are computed at a fixed scale in the SIFT flow method limits its capability of dealing with scenes of large ...
Weichao Qiu +4 more
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The state-of-the-art SIFT flow has been widely adopted for the general image matching task, especially in dealing with image pairs from similar scenes but with different object configurations. However, the way in which the dense SIFT features are computed at a fixed scale in the SIFT flow method limits its capability of dealing with scenes of large ...
Weichao Qiu +4 more
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1995
A segmentation scheme based on tracing objects and borders through scale space is proposed. Scale space allows to create a hierarchical representation of input data which can be used to tessellate input space into objects with closed and orientable borders. For analyzing the structure of scale space, a neural network approach using synchronizing neural
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A segmentation scheme based on tracing objects and borders through scale space is proposed. Scale space allows to create a hierarchical representation of input data which can be used to tessellate input space into objects with closed and orientable borders. For analyzing the structure of scale space, a neural network approach using synchronizing neural
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