A new wavelet-based edge detector via constrained optimization

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Computer Science and Information Engineering > journal & Dissertation > Item 987654321/29193

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29193

Title:	A new wavelet-based edge detector via constrained optimization
Authors:	Hsieh,JW;Ko,MT;Liao,HYM;Fan,KC
Contributors:	資訊工程研究所
Keywords:	LOCALIZATION;FILTERS;DESIGN
Date:	1997
Issue Date:	2010-06-29 20:16:00 (UTC+8)
Publisher:	中央大學
Abstract:	This paper proposes a new wavelet-based approach to solving the edge detection problem. The proposed scheme adopts Canny's three criteria [3] as a guide to derive a wavelet-style edge filter such that the edge points of an image can be detected efficiently and accurately at different scales. Since Canny's criteria are suitable for those edge detectors that detect local extremes, the desired wavelet is, therefore, chosen to be anti-symmetric. In order to obtain sufficient information for reconstructing and analysing the original image the dual of the desired wavelet is also required. Basically, the pair of wavelets is represented as a linear combination of translations of a scaling function. By introducing a constrained optimization process, the set of expansion coefficients of the desired wavelet and its dual as well can be determined. In order to implement the desired edge detector, a continuous wavelet has to be converted into the discrete form. For this purpose the format of the discrete wavelet transform has to be developed. Since the proposed edge filter is wavelet-based, the inherent multiresolution nature of the wavelet transform provides more flexibility on the analysis of images. Also, since an optimization process is introduced in the filter derivation process the performance of the proposed filter is better than that of Mallat-Zhong's edge detector. In real implementation, the experimental results show that the proposed approach is indeed superb. (C) 1997 Elsevier Science B.V.
Relation:	IMAGE AND VISION COMPUTING
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	483	View/Open

社群 sharing

Loading...