Total variation image edge detection

Authors:
Peter Ndajah;Hisakazu Kikuchi
Affiliations:
Graduate School of Science and Technology, Niigata University, Niigata, Japan;Department of Electrical and Electronics Engineering, Niigata University, Niigata, Japan
Venue:
NEHIPISIC'11 Proceeding of 10th WSEAS international conference on electronics, hardware, wireless and optical communications, and 10th WSEAS international conference on signal processing, robotics and automation, and 3rd WSEAS international conference on nanotechnology, and 2nd WSEAS international conference on Plasma-fusion-nuclear physics
Year:
2011

Citing 4
Cited 1

Handbook of Image and Video Processing

Handbook of Image and Video Processing
Digital Image Processing (3rd Edition)

Digital Image Processing (3rd Edition)
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods

Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Feature Extraction & Image Processing, Second Edition

Feature Extraction & Image Processing, Second Edition

A total variation-morphological image edge detection approach

TELE-INFO'11/MINO'11/SIP'11 Proceedings of the 10th WSEAS international conference on Telecommunications and informatics and microelectronics, nanoelectronics, optoelectronics, and WSEAS international conference on Signal processing

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Abstract

We studied total variation anisotropic image edge detection. We derive the total variation functional from first principles rather than from measure theory and distribution theory. The Euler-Lagrange equation of the total variation functional gives a steady state equation. The steady state equation acts as an anistropic filter on an image. We compared the total variation method to the Marr-Hildreth method and found that the total variation method gives stronger edges and greater image detail at any scale than the Marr-Hildreth method. The zero crossing method used to find edge points in the Marr-Hildreth method is not effective for the total variation method. simple thresholding appears to be more effective.