Scale-adaptive detection and local characterization of edges based on wavelet transform

  • Authors:

  • C. Ducottet;T. Fournel;C. Barat

  • Affiliations:

  • Laboratoire Traitement du Signal et Instrumentation, UMR CNRS 5516, Bâtiment F, 10 rue Barrouin, 42000 Saint Etienne, France;Laboratoire Traitement du Signal et Instrumentation, UMR CNRS 5516, Bâtiment F, 10 rue Barrouin, 42000 Saint Etienne, France;Laboratoire Traitement du Signal et Instrumentation, UMR CNRS 5516, Bâtiment F, 10 rue Barrouin, 42000 Saint Etienne, France

  • Venue:
  • Signal Processing - Signal processing in communications
  • Year:
  • 2004

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Abstract

In this paper, we present an edge detection and characterization method based on wavelet transform. This method relies on a modelization of contours as smoothed singularities of three particular types (transition, peak and line). Using the wavelet transform modulus maxima lines of the edge models, position and descriptive parameters of each edge point can be inferred. Indeed, on the one hand, the proposed algorithm allows to detect and locate edges at a locally adapted scale. On the other hand, it is able to identify the type of each detected edge point and to measure both its amplitude and smoothness degree. The latter parameters represent, respectively, the contrast and the blur level of the edge point. Evaluation of the method is performed on both synthetic and real images. Synthetic data are used to investigate the influence of different factors and the sensitivity to noise, whereas real images allow to highlight the performance and interests of the method. In particular, we point out that the measured smoothness degree provides a cue to recover depth from defocused images or a cue to diffusion measurements in images of cloud structures. Moreover, from an indoor scene, we demonstrate the relevance of type identification for segmentation purposes.