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On using an analogy to heat flow for shape extraction

On using an analogy to heat flow for shape extraction
On using an analogy to heat flow for shape extraction
We introduce a novel evolution-based segmentation
algorithm which uses the heat flow analogy to
gain practical advantage. The proposed algorithm consists
of two parts. In the first part, we represent a particular heat
conduction problem in the image domain to roughly segment
the region of interest. Then we use geometric heat
flow to complete the segmentation, by smoothing extracted
boundaries and removing noise inside the prior segmented
region. The proposed algorithm is compared with active
contour models and is tested on synthetic and medical
images. Experimental results indicate that our approach
works well in noisy conditions without pre-processing. It
can detect multiple objects simultaneously. It is also
computationally more efficient and easier to control and
implement in comparison with active contour models.
125-139
direkoglu, cem
b793e59b-4188-44b2-99c5-b4dedc46cfda
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
direkoglu, cem
b793e59b-4188-44b2-99c5-b4dedc46cfda
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

direkoglu, cem and Nixon, Mark S. (2013) On using an analogy to heat flow for shape extraction. Pattern Analysis and Applications, 16, 125-139. (doi:10.1007/s10044-011-0223-0).

Record type: Article

Abstract

We introduce a novel evolution-based segmentation
algorithm which uses the heat flow analogy to
gain practical advantage. The proposed algorithm consists
of two parts. In the first part, we represent a particular heat
conduction problem in the image domain to roughly segment
the region of interest. Then we use geometric heat
flow to complete the segmentation, by smoothing extracted
boundaries and removing noise inside the prior segmented
region. The proposed algorithm is compared with active
contour models and is tested on synthetic and medical
images. Experimental results indicate that our approach
works well in noisy conditions without pre-processing. It
can detect multiple objects simultaneously. It is also
computationally more efficient and easier to control and
implement in comparison with active contour models.

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Published date: 2013
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 356733
URI: http://eprints.soton.ac.uk/id/eprint/356733
PURE UUID: 85ab8e99-bda8-49d7-8cd1-6c3e507575b0
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 01 May 2015 15:38
Last modified: 07 Oct 2020 02:35

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