Characterization of Dirac Edge with New Wavelet Transform
WAA '01 Proceedings of the Second International Conference on Wavelet Analysis and Its Applications
Skeletonization of Ribbon-Like Shapes Based on a New Wavelet Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Construction of wavelets for width-invariant characterization of curves
Pattern Recognition Letters
International Journal of Computer Vision
Boolean derivatives with application to edge detection for imaging systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiscale approach for thinning ridges of fingerprint
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Axial representation of character by using wavelet transform
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Locating vessel centerlines in retinal images using wavelet transform: a multilevel approach
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Skeletonization of fingerprint based-on modulus minima of wavelet transform
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Hi-index | 0.00 |
This paper aims at studying the characterization of Dirac-structure edges with wavelet transform, and selecting the suitable wavelet functions to detect them. Three significant characteristics of the local maximum modulus of the wavelet transform with respect to the Dirac-structure edges are presented: (1) slope invariant: the local maximum modulus of the wavelet transform of a Dirac-structure edge is independent on the slope of the edge; (2) grey-level invariant: the local maximum modulus of the wavelet transform with respect to a Dirac-structure edge takes place at the same points when the images with different grey-levels are processed; and (3) width light-dependent: for various widths of the Dirac-structure edge images, the location of maximum modulus of the wavelet transform varies lightly under the certain circumscription that the scale of the wavelet transform is larger than the width of the Dirac-structure edges. It is important, in practice, to select the suitable wavelet functions, according to the structures of edges. For example, Haar wavelet is better to represent brick-like images than other wavelets. A mapping technique is applied in this paper to construct such a wavelet function. In this way, a low-pass function is mapped onto a wavelet function by a derivation operation. In this paper, the quadratic spline wavelet is utilized to characterize the Dirac-structure edges and a novel algorithm to extract the Dirac-structure edges by wavelet transform is also developed