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A Robust Nonparametric Estimation Framework for Implicit Image Models Himanshu Arora Maneesh Singh Narendra Ahuja
 

Summary: A Robust Nonparametric Estimation Framework for Implicit Image Models
Himanshu Arora Maneesh Singh Narendra Ahuja
University of Illinois Siemens Corporate Research University of Illinois
Urbana, IL61801, USA Princeton, NJ08540, USA Urbana, IL61801, USA
harora1@uiuc.edu msingh@scr.siemens.com n-ahuja@uiuc.edu
Abstract
Robust model fitting is important for computer vision tasks
due to the occurrence of multiple model instances, and, un-
known nature of noise. The linear errors-in-variables (EIV)
model is frequently used in computer vision for model fit-
ting tasks. This paper presents a novel formalism to solve
the problem of robust model fitting using the linear EIV
framework. We use Parzen windows to estimate the noise
density and use a maximum likelihood approach for robust
estimation of model parameters. Robustness of the algo-
rithm results from the fact that density estimation helps us
admit an a priori unknown multimodal density function and
parameter estimation reduces to estimation of the density
modes. We also propose a provably convergent iterative
algorithm for this task. The algorithm increases the like-

  

Source: Ahuja, Narendra - Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences; Engineering