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2009 Canadian Conference on Electrical and Computer Engineering, 2009
The Random Walks image segmentation algorithm provides a fast and effective method for supervised image segmentation. However, Random Walks does not work very well in the presence of noise or texture. Therefore, we propose an augmented version of Random Walks known as “Scale-Space Random Walks” (SSRW) that addresses these problems.
Richard Rzeszutek +2 more
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The Random Walks image segmentation algorithm provides a fast and effective method for supervised image segmentation. However, Random Walks does not work very well in the presence of noise or texture. Therefore, we propose an augmented version of Random Walks known as “Scale-Space Random Walks” (SSRW) that addresses these problems.
Richard Rzeszutek +2 more
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Phase singularities in scale-space
Image and Vision Computing, 1991Abstract This paper concerns the use of phase information from band-pass signals for the measurement of binocular disparity, optic flow and image orientation. Towards this end, one of the important properties of band-pass phase information is its stability with respect to small geometric deformations and contrast changes.
Allan D. Jepson, David J. Fleet
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Optical flow in the scale space
1997There exists optical flow in the multiscale representation of an image if this representation is viewed as an image sequence in the “time” domain. The ill-posed tracking problem in the scale-space can be robustly solved by this method. The perceptual effect of any multiscale representation can be greatly improved by the so-called “pull-back” technique.
Qing Yang 0002, Songde Ma
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Clustering by scale-space filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000In pattern recognition and image processing, the major application areas of cluster analysis, human eyes seem to possess a singular aptitude to group objects and find important structures in an efficient and effective way. Thus, a clustering algorithm simulating a visual system may solve some basic problems in these areas of research.
Yee Leung +2 more
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Scale-space filters and their robustness
1997We discuss some properties of a class of scale-space processors called sieves which are useful because, like a diffusion processor, they have an increasing support region, but, unlike a diffusion processor, the region follows extremal regions in the image. We test their robustness to noise and occlusion.
Richard W. Harvey +2 more
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Scale-Space Clustering on the Sphere
2013We present an algorithm for scale-space clustering of point cloud on the sphere using the methodology for the estimation of the density distribution of the points in the linear scale space. Our algorithm regards the union of observed point sets as an image defined by the delta functions located at the positions of the points on the sphere.
Yoshihiko Mochizuki +4 more
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2002
Scale spaces allow us to organize, compare and analyse differently sized structures of an object. In this work, we present and compare five ways of discretizing the Gaussian scale-space: sampling Gaussian distributions; recursively calculating Gaussian approximations; using Splines; approximating by first-order generators; and finally, by a new method ...
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Scale spaces allow us to organize, compare and analyse differently sized structures of an object. In this work, we present and compare five ways of discretizing the Gaussian scale-space: sampling Gaussian distributions; recursively calculating Gaussian approximations; using Splines; approximating by first-order generators; and finally, by a new method ...
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2007
Inverse scale space methods are derived as asymptotic limits of iterative regularization methods. They have proven to be efficient methods for denoising of gray valued images and for the evaluation of unbounded operators. In the beginning, inverse scale space methods have been derived from iterative regularization methods with squared Hilbert norm ...
Klaus Frick, Otmar Scherzer
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Inverse scale space methods are derived as asymptotic limits of iterative regularization methods. They have proven to be efficient methods for denoising of gray valued images and for the evaluation of unbounded operators. In the beginning, inverse scale space methods have been derived from iterative regularization methods with squared Hilbert norm ...
Klaus Frick, Otmar Scherzer
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Stereo correspondences in scale space
1997A central problem in stereo matching using correlation techniques lies in selecting the size of the search window. Small windows contain only a small number of data points, and thus are very sensitive to noise and therefore result in false matches.
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On Manifolds in Gaussian Scale Space
2003In an ordinary 2D image the critical points and the isophotes through the saddle points provide sufficient information for classifying the image into distinct regions belonging to the extrema (i.e. a collection of bright and dark blobs), together with their nesting due to the saddle isophotes.
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