Edge Extraction of Images by Reconstruction Using Wavelet Decomposition Details at Different Resolution Levels

L. Feng, C. Y. Suen, Yuan Yan Tang, Lihua Yang

Research output: Contribution to journalJournal articlepeer-review

38 Citations (Scopus)

Abstract

This paper describes a novel method for edge feature detection of document images based on wavelet decomposition and reconstruction. By applying the wavelet decomposition technique, a document image becomes a wavelet representation, i.e. the image is decomposed into a set of wavelet approximation coefficients and wavelet detail coefficients. Discarding wavelet approximation, the edge extraction is implemented by means of the wavelet reconstruction technique. In consideration of the mutual frequency, overlapping will occur between wavelet approximation and wavelet details, a multiresolution-edge extraction with respect to an iterative reconstruction procedure is developed to ameliorate the quality of the reconstructed edges in this case. A novel combination of this multiresolution-edge results in clear final edges of the document images. This multi-resolution reconstruction procedure follows a coarser-to-finer searching strategy. The edge feature extraction is accompanied by an energy distribution estimation from which the levels of wavelet decomposition are adaptively controlled. Compared with the scheme of wavelet transform, our method does not incur any redundant operation. Therefore, the computational time and the memory requirement are less than those in wavelet transform.

Original languageEnglish
Pages (from-to)779-793
Number of pages15
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume14
Issue number6
DOIs
Publication statusPublished - Sept 2000

User-Defined Keywords

  • Wavelet decomposition and reconstruction
  • wavelet approximation
  • wavelet details
  • energy estimation
  • adaptive edge enhancement (AEE)

Fingerprint

Dive into the research topics of 'Edge Extraction of Images by Reconstruction Using Wavelet Decomposition Details at Different Resolution Levels'. Together they form a unique fingerprint.

Cite this