Robust regression and outlier detection
Robust regression and outlier detection
Robust regression methods for computer vision: a review
International Journal of Computer Vision
Parametric Model Fitting: From Inlier Characterization to Outlier Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional Digital Signal Processing II: Transforms and Median Filters
Two-Dimensional Digital Signal Processing II: Transforms and Median Filters
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Asymptotic MAP criteria for model selection
IEEE Transactions on Signal Processing
A robust nonlinear filter for image restoration
IEEE Transactions on Image Processing
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A structure-adaptive approach to robust statistical estimation of image intensity for adaptive filtering and segmentation of images is described in the context of a two-region structural image model. The proposed adaptive estimation procedure is based on the selection of best fitting structuring region relatively to a current point from all available multiple structuring regions by the maximum a posteriori probability principle. In application to image filtering, the described method allows to suppress noise and, at the same time, not damage the initial image including corner edges and image fine details. It provides also a robust binary segmentation of local objects of interest and their edges on noisy background.