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Low Level Moving-Feature Extraction Via Heat Flow Analogy

Low Level Moving-Feature Extraction Via Heat Flow Analogy
Low Level Moving-Feature Extraction Via Heat Flow Analogy
In this paper, an intelligent and automatic moving object edge detection algorithm is proposed, based on 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 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 results indicate that this approach has advantages in handling 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.
feature extraction, motion
978-3-540-48628-2
243-252
Direkoglu, Cem
b793e59b-4188-44b2-99c5-b4dedc46cfda
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Bebis, G.
da971361-1950-4b15-9d36-214fe9256372
Direkoglu, Cem
b793e59b-4188-44b2-99c5-b4dedc46cfda
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Bebis, G.
da971361-1950-4b15-9d36-214fe9256372

Direkoglu, Cem and Nixon, Mark S. (2006) Low Level Moving-Feature Extraction Via Heat Flow Analogy. Bebis, G. (ed.) 2nd International Symposium on Visual Computing, LNCS, United States. 06 - 08 Nov 2006. pp. 243-252 .

Record type: Conference or Workshop Item (Other)

Abstract

In this paper, an intelligent and automatic moving object edge detection algorithm is proposed, based on 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 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 results indicate that this approach has advantages in handling 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.

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More information

Published date: 2006
Additional Information: Event Dates: November 6-8, 2006
Venue - Dates: 2nd International Symposium on Visual Computing, LNCS, United States, 2006-11-06 - 2006-11-08
Keywords: feature extraction, motion
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 263717
URI: http://eprints.soton.ac.uk/id/eprint/263717
ISBN: 978-3-540-48628-2
PURE UUID: 55aab9dd-8965-4569-8bc6-ec3eb5d58da6
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 17 Mar 2007
Last modified: 20 Jul 2019 01:28

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