Moving-edge detection via heat flow analogy
Moving-edge detection via heat flow analogy
In this paper, a new and automatic moving-edge detection algorithm is proposed, based on using the heat flow analogy. This algorithm starts with anisotropic heat diffusion in the spatial domain, to remove noise and sharpen region boundaries for the purpose of obtaining high quality edge data. Then, isotropic and linear heat diffusion is applied in the temporal domain to calculate the total amount of heat flow. The moving-edges are represented as the total amount of heat flow out from the reference frame. The overall process is completed by non-maxima suppression and hysteresis thresholding to obtain binary moving edges. Evaluation, on a variety of data, indicates that this approach can handle noise in the temporal domain because of the averaging inherent of isotropic heat flow. Results also show that this technique can detect moving-edges in image sequences, without background image subtraction.
Moving-edges Feature extraction Image processing Computer vision Heat flow
270-279
Direkoglu, Cem
b793e59b-4188-44b2-99c5-b4dedc46cfda
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
2011
Direkoglu, Cem
b793e59b-4188-44b2-99c5-b4dedc46cfda
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Direkoglu, Cem and Nixon, Mark
(2011)
Moving-edge detection via heat flow analogy.
Pattern Recognition Letters, 32, .
Abstract
In this paper, a new and automatic moving-edge detection algorithm is proposed, based on using the heat flow analogy. This algorithm starts with anisotropic heat diffusion in the spatial domain, to remove noise and sharpen region boundaries for the purpose of obtaining high quality edge data. Then, isotropic and linear heat diffusion is applied in the temporal domain to calculate the total amount of heat flow. The moving-edges are represented as the total amount of heat flow out from the reference frame. The overall process is completed by non-maxima suppression and hysteresis thresholding to obtain binary moving edges. Evaluation, on a variety of data, indicates that this approach can handle noise in the temporal domain because of the averaging inherent of isotropic heat flow. Results also show that this technique can detect moving-edges in image sequences, without background image subtraction.
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Published date: 2011
Keywords:
Moving-edges Feature extraction Image processing Computer vision Heat flow
Organisations:
Vision, Learning and Control
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Local EPrints ID: 271877
URI: http://eprints.soton.ac.uk/id/eprint/271877
PURE UUID: 684ab5c6-9e80-49a3-a475-45220305d66a
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Date deposited: 07 Jan 2011 14:43
Last modified: 15 Mar 2024 02:35
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Author:
Cem Direkoglu
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