Differentiation-Based Edge DetectionUsing the Logarithmic Image Processing Model

  • Authors:

  • Guang Deng;Jean-Charles Pinoli

  • Affiliations:

  • School of Electronic Engineering, La Trobe University, Bundoora Victoria 3083, Australia. E-mail: d.deng@ee.latrobe.edu.au;Pechiney, Centre de Recherches, BP 27, F38340 Voreppe, France/ and Laboratoire Image, Signal et Acoustique, CNRS EP92, Ecole Supé/rieure de Chimie, Physique et Electronique, 31 Place Bellecour ...

  • Venue:
  • Journal of Mathematical Imaging and Vision
  • Year:
  • 1998

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Abstract

The logarithmic image processing (LIP) model is a mathematical framework which provides a specific set of algebraic and functional operations for the processing and analysis of intensity images valued in abounded range. The LIP model has been proved to be physically justified bythat it is consistent with the multiplicative transmittance and reflectanceimage formation models, and with some important laws and characteristics ofhuman brightness perception. This article addresses the edge detectionproblem using the LIP-model based differentiation. First, the LIP model isintroduced, in particular, for the gray tones and gray tone functions, whichrepresent intensity values and intensity images, respectively. Then, anextension of these LIP model notions, respectively called gray tone vectorsand gray tone vector functions, is studied. Third, the LIP-model baseddifferential operators are presented, focusing on their distinctiveproperties for image processing. Emphasis is also placed on highlighting themain characteristics of the LIP-model based differentiation. Next, theLIP-Sobel based edge detection technique is studied and applied to edgedetection, showing its robustness in locally small changes in sceneillumination conditions and its performance in the presence of noise. Itstheoretical and practical advantages over several well-known edge detectiontechniques, such as the techniques of Sobel, Canny, Johnson and Wallis, areshown through a general discussion and illustrated by simulation results ondifferent real images. Finally, a discussion on the role of the LIP-modelbased differentiation in the current context of edge detection ispresented.