A Robust Probabilistic Estimation Framework for Parametric Image Models

  • Maneesh Singh
  • Himanshu Arora
  • Narendra Ahuja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3021)


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.


Image Model Range Image Recursive Little Square Less Square Estimator Kernel Density Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Maneesh Singh
    • 1
  • Himanshu Arora
    • 1
  • Narendra Ahuja
    • 1
  1. 1.University of Illinois at Urbana-ChampaignUrbanaUSA

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