Parameter estimation in the polynomial regression model by aggregation of partial optimal estimates

Roman M. Palenichka; Peter Zinterhof

doi:10.1117/12.323442

24 September 1998 Parameter estimation in the polynomial regression model by aggregation of partial optimal estimates

Roman M. Palenichka, Peter Zinterhof

Proceedings Volume 3457, Mathematical Modeling and Estimation Techniques in Computer Vision; (1998) https://doi.org/10.1117/12.323442
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

Robust statistical estimators have found wide application in image processing and computer vision because conventional estimation methods fail to work when outliers from the assumed image model are present in real image data. In this paper, the method of partial robust estimates is described in which the final estimate of model parameters is made by the concept of maximum a posteriori probability or by the adaptive linear combination depending on the image contents. The underlying image model consists of a polynomial regression representation of the image intensity function and a structural model of local objects on non-homogeneous background. The developed estimation procedures have been tested on radiographic images in applications to detail-preserving smoothing and detection of local objects of interest. The obtained results and theoretical investigation confirm the model adequacy to real image data and robustness of the developed estimators of the model parameters.

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Roman M. Palenichka and Peter Zinterhof "Parameter estimation in the polynomial regression model by aggregation of partial optimal estimates", Proc. SPIE 3457, Mathematical Modeling and Estimation Techniques in Computer Vision, (24 September 1998); https://doi.org/10.1117/12.323442

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KEYWORDS

Statistical analysis

Data modeling

Image processing

Smoothing

Visual process modeling

Signal processing

Statistical modeling

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