NDLI: An Iterative Approach to Improved Local Phase Coherence Estimation

Content Provider	IEEE Xplore Digital Library
Author	Wong, A.
Copyright Year	2008
Description	Author affiliation: Syst. Design Eng., Waterloo Univ., Waterloo, ON (Wong, A.)
Abstract	This paper introduces a novel iterative approach to estimating local phase coherence in situations characterized by low signal-to-noise ratios. Local phase coherence is used for a wide range of computer vision applications such as edge and corner detection and object description. An issue faced in extracting local phase coherence is the presence of image noise. While existing approaches to dealing with noise when estimating local phase coherence is effective for low noise situations, they are inadequate for situations contaminated by high levels of noise. In the proposed approach, the issue of high image noise is addressed by re-estimating both the local phase coherence and the underlying image content iteratively to improve local phase coherence estimates. This is performed using a feedback loop model, where the local phase coherence estimates are used to re-estimate the image content using a moment-adaptive bilateral estimation scheme and information from the re-estimated image content is used to re-estimate the local phase coherence. Experiment results show that the proposed approach can be used to provide improved local phase coherence estimates.
Starting Page	301
Ending Page	307
File Size	4314746
Page Count	7
File Format	PDF
ISBN	9780769531533
DOI	10.1109/CRV.2008.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-28
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Phase noise Computer vision image Image edge detection iterative phase coherence Application software bilaterial estimation structure Noise level Phase estimation Coherence Object detection computer vision Iterative methods Signal to noise ratio
Content Type	Text
Resource Type	Article

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