Feature-preserving flows: A stochastic differential equation's view

G. B. Unal; H. Krim; A. Yezzi

Feature-preserving flows: A stochastic differential equation's view

G. B. Unal
, H. Krim^*
, A. Yezzi

^*Corresponding author for this work

North Carolina State University

Research output: Contribution to conference › Paper › peer-review

5 Citations (Scopus)

Abstract

Evolution equations have proven to be useful in tracking fine to coarse features in a single level curve and/or in an image. In this paper, we give a stochastic insight to a specific evolution equation, namely the geometric heat equation, and subsequently use this insight to develop a class of feature-driven diffusions. A progressive smoothing along desired features of a level curve is aimed at overcoming effects of noisy environment during feature extraction and denoising applications.

Original language	English
Pages	896-899
Number of pages	4
Publication status	Published - 2000
Externally published	Yes
Event	International Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada Duration: 10 Sept 2000 → 13 Sept 2000

Conference

Conference	International Conference on Image Processing (ICIP 2000)
Country/Territory	Canada
City	Vancouver, BC
Period	10/09/00 → 13/09/00

Cite this

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title = "Feature-preserving flows: A stochastic differential equation's view",

abstract = "Evolution equations have proven to be useful in tracking fine to coarse features in a single level curve and/or in an image. In this paper, we give a stochastic insight to a specific evolution equation, namely the geometric heat equation, and subsequently use this insight to develop a class of feature-driven diffusions. A progressive smoothing along desired features of a level curve is aimed at overcoming effects of noisy environment during feature extraction and denoising applications.",

author = "Unal, \{G. B.\} and H. Krim and A. Yezzi",

year = "2000",

language = "English",

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note = "International Conference on Image Processing (ICIP 2000) ; Conference date: 10-09-2000 Through 13-09-2000",

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TY - CONF

T1 - Feature-preserving flows

T2 - International Conference on Image Processing (ICIP 2000)

AU - Unal, G. B.

AU - Krim, H.

AU - Yezzi, A.

PY - 2000

Y1 - 2000

N2 - Evolution equations have proven to be useful in tracking fine to coarse features in a single level curve and/or in an image. In this paper, we give a stochastic insight to a specific evolution equation, namely the geometric heat equation, and subsequently use this insight to develop a class of feature-driven diffusions. A progressive smoothing along desired features of a level curve is aimed at overcoming effects of noisy environment during feature extraction and denoising applications.

AB - Evolution equations have proven to be useful in tracking fine to coarse features in a single level curve and/or in an image. In this paper, we give a stochastic insight to a specific evolution equation, namely the geometric heat equation, and subsequently use this insight to develop a class of feature-driven diffusions. A progressive smoothing along desired features of a level curve is aimed at overcoming effects of noisy environment during feature extraction and denoising applications.

UR - https://www.scopus.com/pages/publications/0034442895

M3 - Paper

AN - SCOPUS:0034442895

SP - 896

EP - 899

Y2 - 10 September 2000 through 13 September 2000

ER -

Feature-preserving flows: A stochastic differential equation's view

Abstract

Conference

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