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Proceedings Paper

A New Family Of Nonlinear Edge Detectors For Noisy Images
Author(s): Nobuyuki Otsu; Tony Kasvand
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Paper Abstract

Many methods have been proposed to enhance and detect edges in gray-level images. Most of these are based on spatial differentiation to enchance gray-level changes, and have the common problem of being very sensitive to noise. In order to surpress the noise, some spa-tial averaging has been combined with the differentiation. This, however, tends to complicate the definition of the operator and also results in not very sharp edges. In this paper, we consider the characteristics of edges and the statistical properties of noise in gray-level histograms taken from local regions of an image. The noise can then be regarded as a blurring process on the gray-level histograms. Therefore, the problem of edge detection for noisy images can be reduced to the problem of invariant feature extraction under one-dimensional blurring. By applying a theory of blurring-invariant feature extraction, a new family of nonlinear edge detectors is derived. The resultant operators are simple, based on central moments of the gray levels within a local window, and definable independently of edge orientation in the window. The operators are quite insensitive to noise, and their effectiveness has been confirmed by experiments.

Paper Details

Date Published: 9 January 1984
PDF: 8 pages
Proc. SPIE 0435, Architectures and Algorithms for Digital Image Processing, (9 January 1984); doi: 10.1117/12.936958
Show Author Affiliations
Nobuyuki Otsu, Electrotechnical Laboratory (Japan)
Tony Kasvand, National Research Council (Canada)

Published in SPIE Proceedings Vol. 0435:
Architectures and Algorithms for Digital Image Processing
Per-Erik Danielsson; Andre J. Oosterlinck, Editor(s)

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