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Heat kernel smoothing of scalar and vector image data

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Heat kernel smoothing of scalar and vector image data. / Zhang, Fan; Hancock, Edwin R.

In: 2006 IEEE International Conference on Image Processing, ICIP 2006, Proceedings, 2006, p. 1549-1552.

Research output: Contribution to journalArticle

Harvard

Zhang, F & Hancock, ER 2006, 'Heat kernel smoothing of scalar and vector image data', 2006 IEEE International Conference on Image Processing, ICIP 2006, Proceedings, pp. 1549-1552.

APA

Zhang, F., & Hancock, E. R. (2006). Heat kernel smoothing of scalar and vector image data. 2006 IEEE International Conference on Image Processing, ICIP 2006, Proceedings, 1549-1552.

Vancouver

Zhang F, Hancock ER. Heat kernel smoothing of scalar and vector image data. 2006 IEEE International Conference on Image Processing, ICIP 2006, Proceedings. 2006;1549-1552.

Author

Zhang, Fan ; Hancock, Edwin R. / Heat kernel smoothing of scalar and vector image data. In: 2006 IEEE International Conference on Image Processing, ICIP 2006, Proceedings. 2006 ; pp. 1549-1552.

Bibtex - Download

@article{2b61fcefe1e94f3f8c3623164588d847,
title = "Heat kernel smoothing of scalar and vector image data",
abstract = "This paper shows how the graph-spectral heat kernel can be used to smooth both gray-scale and color images. We represent images using weighted attributed graphs, and compute the associated Laplacian matrix. Diffusion across this weighted graph-structure with time is captured by the heat-equation, and the solution, i.e. the heat kernel, is found by exponentiating the Laplacian eigen-system with time. Image smoothing is effected by convolving the heat kernel with the image. The method has the effect of smoothing within regions, but does not blur region boundaries. Experiments and comparisons on standard images illustrate the effectiveness of the method.",
keywords = "image restoration, image enhancement, smoothing methods, filter noise, ANISOTROPIC DIFFUSION, EDGE-DETECTION",
author = "Fan Zhang and Hancock, {Edwin R.}",
year = "2006",
language = "English",
pages = "1549--1552",
journal = "2006 IEEE International Conference on Image Processing, ICIP 2006, Proceedings",
issn = "1522-4880",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Heat kernel smoothing of scalar and vector image data

AU - Zhang, Fan

AU - Hancock, Edwin R.

PY - 2006

Y1 - 2006

N2 - This paper shows how the graph-spectral heat kernel can be used to smooth both gray-scale and color images. We represent images using weighted attributed graphs, and compute the associated Laplacian matrix. Diffusion across this weighted graph-structure with time is captured by the heat-equation, and the solution, i.e. the heat kernel, is found by exponentiating the Laplacian eigen-system with time. Image smoothing is effected by convolving the heat kernel with the image. The method has the effect of smoothing within regions, but does not blur region boundaries. Experiments and comparisons on standard images illustrate the effectiveness of the method.

AB - This paper shows how the graph-spectral heat kernel can be used to smooth both gray-scale and color images. We represent images using weighted attributed graphs, and compute the associated Laplacian matrix. Diffusion across this weighted graph-structure with time is captured by the heat-equation, and the solution, i.e. the heat kernel, is found by exponentiating the Laplacian eigen-system with time. Image smoothing is effected by convolving the heat kernel with the image. The method has the effect of smoothing within regions, but does not blur region boundaries. Experiments and comparisons on standard images illustrate the effectiveness of the method.

KW - image restoration

KW - image enhancement

KW - smoothing methods

KW - filter noise

KW - ANISOTROPIC DIFFUSION

KW - EDGE-DETECTION

M3 - Article

SP - 1549

EP - 1552

JO - 2006 IEEE International Conference on Image Processing, ICIP 2006, Proceedings

JF - 2006 IEEE International Conference on Image Processing, ICIP 2006, Proceedings

SN - 1522-4880

ER -