Abstract

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 para- metric models may only be valid on individual segments of the image. Consequently, we model noise using a nonparametric kernel density esti- mation framework and use a locally or globally linear parametric model to represent the noise-free image pattern. This results in a novel, ro- bust, 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.