Models of spatial variation in images are central to a large number of low-level computer vision problems including segmentation, registration, and 3D structure detection. Often, images are represented using parametric models to characterize (noise-free) image variation, and, additive noise. However, the noise model may be unknown and parametric models may only be valid on individual segments of the image. Consequently, we model noise using a nonparametric kernel density estimation framework and use a locally or globally linear parametric model to represent the noise-free image pattern. This results in a novel, robust, redescending, M- parameter estimator for the above image model which we call the Kernel Maximum Likelihood estimator (KML). We also provide a provably convergent, iterative algorithm for the resultant optimization problem. The estimation framework is empirically validated on synthetic data and applied to the task of range image segmentation. © Springer-Verlag 2004.
CITATION STYLE
Singh, M., Arora, H., & Ahuja, N. (2004). A robust probabilistic estimation framework for parametric image models. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3021, 508–522. https://doi.org/10.1007/978-3-540-24670-1_39
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