Advanced SearchSearch Tips
Wavelet-Based Edge Detection Using Local Histogram Analysis in Images
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Wavelet-Based Edge Detection Using Local Histogram Analysis in Images
Park, Min-Joon; Kwon, Min-Jun; Kim, Gi-Hun; Shim, Han-Seul; Kim, Dong-Wook; Lim, Dong-Hoon;
  PDF(new window)
Edge detection in images is an important step in image segmentation and object recognition as preprocessing for image processing. This paper presents a new edge detection using local histogram analysis based on wavelet transform. In this work, the wavelet transform uses three components (horizontal, vertical and diagonal) to find the magnitude of the gradient vector, instead of the conventional approach in which tw components are used. We compare the magnitude of the gradient vector with the threshold that is obtained from a local histogram analysis to conclude that an edge is present or not. Some experimental results for our edge detector with a Sobel, Canny, Scale Multiplication, and Mallat edge detectors on sample images are given and the performances of these edge detectors are compared in terms of quantitative and qualitative measures. Our detector performs better than the other wavelet-based detectors such as Scale Multiplication and Mallat detectors. Our edge detector also preserves a good performance even if the Sobel and Canny detector are sharply low when the images are highly corrupted.
Wavelet;wavelet transform;local histogram analysis;Mallat detector;edge detection;
 Cited by
Bao, P., Zhang, L. and Wu, X. (2005). Canny edge detection enhancement by scale multiplication, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 1485-1490. crossref(new window)

Canny, J. (1986). A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8, 679-698. crossref(new window)

Elmabrouk, A. and Aggoun, A. (1998). Edge detection using local histogram analysis, Electronic Letters, 34, 1216-1217. crossref(new window)

Gonzalez, R. C. and Woods, R. E. (1993). Digital Image Processing, Addison-Wesley Publishing Company.

Khallil, M. and Aggoun, A. (2006). Edge detection using adaptive local histogram analysis, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 45-48.

Lee, S. U., Chung S. Y. and Park, R. H. (1990). A comparative performance study of several global thresh-olding techniques for segmentation, Computer Vision, Graphics, and Image Processing, 52, 171-190. crossref(new window)

Lim, D. H. (2006). Robust edge detection in noisy images, Computational Statistics and Data Analysis, 50, 803-812. crossref(new window)

Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674-693. crossref(new window)

Mallat, S. G. (1999). A Wavelet Tour of Signal Processing, Academic Press

Mallat, S. and Hwang, W. L. (1992). Singularity detection and processing with wavelets, IEEE Transactions of Information Theory, 38, 617-643. crossref(new window)

Mallat, S. and Zhong, S. (1992). Characterization of signals from multiscale edges, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 710-732. crossref(new window)

Nabti, M., Ghouti, L. and Bouridane, A. (2006). Multiscale edge detection using wavelet maxima for iris localization, IEE Visual Information Engineering, 62-67.

Otsu, N. (1979). A threshold selection method from gray-level histograms, IEEE Transactions on In Systems, Man and Cybernetics, 9, 62-66. crossref(new window)

Pratt, W. (1978). Digital Image Processing, John Wiley & Sons, 538-543.

Voorhees, H. and Poggio, T. (1987). Detecting textons and texture boundaries in natural images, Proceedings of the First International Conference on Computer Vision, 250-258.

Wang, Y., Adah, T. and Lau, C. (2002). Automatic threshold selection using histogram quantization, Journal of Biomedical Optics, 211-217.

Zhang, L. and Bao, P. (2002). Edge detection by scale multiplication in wavelet domain, Pattern Recoginition Letters, 23, 1771-1784. crossref(new window)

Zhu, Z., Lu, H. and Zhao, Y. (2007). Scale multiplication in odd Gabor transform domain for edge detection, Journal of Visual Communication and Image Representation, 18, 68-80. crossref(new window)