posted on 2017-12-06, 00:00authored byWanwu Guo, A Watson
We recently reported the trial work using the conjugate directional filtering (CDF) to combine two directional-filtered results in conjugate directions into one image that exhibits the maximum linear features in the two conjugate directions. Our further study reveals that the CDF has some weaknesses although it is useful for the enhancement of conjugated linear features. The CDF came initially without consideration of using a weighting system for further data manipulation during operation. CDF-processed image often lacks contrast depth because most background information is removed. In this paper, we present a modification of CDF, named modified conjugate directional filtering (MCDF), in which a weighting system is adopted. This adoption allows not only the linear features in the two conjugate directions to be treated differently, but also some subtle features to be identified. This modification also leads to a proportional combination of a weighted CDF data file and its original file to produce an image on which not only enhanced are the conjugate linear features, but also retained is all the information on the original image. This also makes a MCDF image show 3D effect. A synthetic example is used to test the improvement of the MCDF over the CDF.
Funding
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
History
Parent Title
Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition, and Application, June 25-28 2002, Crete, Greece.
Start Page
331
End Page
334
Number of Pages
4
Start Date
2002-01-01
ISBN-10
0889863385
Location
Crete, Greece
Publisher
ACTA Press
Place of Publication
Calgary, Canada
Peer Reviewed
Yes
Open Access
No
External Author Affiliations
Edith Cowan University; International Conference on Signal Processing, Pattern Recognition, and Application;
Era Eligible
No
Name of Conference
International Association for Science and Technology for Development. International Conference on Signal Processing, Pattern Recognition, and Application