It is a common practice in computer vision and image processing to convolve rectangular constant coefficient windows with digital images to perform local smoothing and derivative estimation for edge detection and other purposes. If all data points in each image window belong to the same statistical population, this practice is reasonable and fast. But, as is well known, constant coefficient window operators produce incorrect results if more than one statistical population is present within a window, for example, if a gray-level or gradient discontinuity is present. This paper shows one way to apply the theory of robust statistics to the data smoothing and derivative estimation problem. A robust window operator is demonstrated that preserves gray-level and gradient discontinuities in digital images as it smooths and estimates derivatives.

Robust Window Operators

Robust window operators

It is a common practice in computer vision and image processing to convolve rectangular constant coefficient windows with digital images to perform local smoothing and derivative estimation for edge detection and other purposes. If all data points in each image window belong to the same statistical population, this practice is reasonable and fast. But, as is well known, constant coefficient window operators produce incorrect results if more than one statistical population is present within a window, e.g., if a gray level or gradient discontinuity is present. This paper shows one way to apply the theory of robust statistics to the data smoothing and derivative estimation problem. A robust window operator is demonstrated that preserves gray level and gradient discontinuities in digital images as it smooths and estimates derivatives.

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A Robust Variable Order Facet Model for Image Data

Most of the parametric techniques of the spectrum estimation assume a specific distribution for the observations. Even a small number of outliers in the observations, violating the distribution assumption can yield poor spectral estimates. We present an algorithm which yields 2-D spectral estimates, robust to additive outliers. The algorithm is iterative in nature and involves fitting a 2-D noncausal spatial autoregressive (SAR) to the given data.

R. Chellappa

Papers

An iterative algorithm for robust 2-D spectrum estimation