Extracting Arbitrary Geometric Primitives Represented by Fourier Descriptors
Extracting Arbitrary Geometric Primitives Represented by Fourier Descriptors
In this paper we present a novel formulation for the extraction of arbitrary shapes in model-based recognition. The formulation is based on the mapping defined in the Hough transform. We develop this mapping for the analytic representation of a shape characterised by a Fourier parameterisation. Edge direction information is included in the formulation as a way of reducing the computational requirements in the extraction process. The proposed approach extends the analytic formulation of the Hough transform to arbitrary shapes which leads to an accurate and efficient evidence accumulation process. Experimental results show that the new approach can handle noise and occlusion in synthetic and real images.
547--551
Aguado, A.S.
ad7e99c5-47ab-4f88-849a-c6e6d77e4200
Montiel, M.E.
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Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
1996
Aguado, A.S.
ad7e99c5-47ab-4f88-849a-c6e6d77e4200
Montiel, M.E.
4aefa43d-aeb9-4151-83f5-31fbab5664ba
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Aguado, A.S., Montiel, M.E. and Nixon, M.S.
(1996)
Extracting Arbitrary Geometric Primitives Represented by Fourier Descriptors.
Proc. International Conference on Pattern Recognition ICPR '96.
.
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Conference or Workshop Item
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Abstract
In this paper we present a novel formulation for the extraction of arbitrary shapes in model-based recognition. The formulation is based on the mapping defined in the Hough transform. We develop this mapping for the analytic representation of a shape characterised by a Fourier parameterisation. Edge direction information is included in the formulation as a way of reducing the computational requirements in the extraction process. The proposed approach extends the analytic formulation of the Hough transform to arbitrary shapes which leads to an accurate and efficient evidence accumulation process. Experimental results show that the new approach can handle noise and occlusion in synthetic and real images.
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Published date: 1996
Additional Information:
Address: Vienna
Venue - Dates:
Proc. International Conference on Pattern Recognition ICPR '96, 1996-01-01
Organisations:
Southampton Wireless Group
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Local EPrints ID: 250060
URI: http://eprints.soton.ac.uk/id/eprint/250060
PURE UUID: 4ccd3d37-e2ac-4269-88cd-8df7cef8bea3
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Date deposited: 04 May 1999
Last modified: 09 Jan 2022 02:33
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Contributors
Author:
A.S. Aguado
Author:
M.E. Montiel
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