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GLOBAL PERFORMANCE PREDICTION FOR DIVERGENCE-BASED IMAGE REGISTRATION CRITERIA.

Kumar Sricharan1, Raviv Raich2, Alfred O Hero1

  • 1Department of EECS, University of Michigan, Ann Arbor, MI 48109.

... IEEE Statistical Signal Processing Workshop. IEEE Statistical Signal Processing Workshop
|April 24, 2015
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Summary

We introduce a novel k-nearest neighbors (kNN) density estimation method for accurately estimating divergence measures. This approach provides a theoretical basis for analyzing image registration techniques, improving machine learning and signal processing applications.

Keywords:
divergence estimationkNN density estimatorsperformance characterizationplug-in estimators

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Area of Science:

  • Statistics
  • Machine Learning
  • Signal Processing

Background:

  • Divergence measures are crucial in statistics, signal processing, and machine learning.
  • Existing estimators for divergence measures lack performance analysis.

Purpose of the Study:

  • To propose and analyze a novel k-nearest neighbors (kNN) density estimation-based plug-in estimator for divergence measures.
  • To establish a theoretical foundation for evaluating image registration methods.

Main Methods:

  • Utilizing kNN density estimation for a plug-in estimator of divergence measures.
  • Deriving the bias, variance, and mean squared error (MSE) of the estimator.
  • Analyzing the estimator's convergence in distribution.

Main Results:

  • The bias, variance, and MSE are characterized in terms of sample size, dimension, and probability distribution.
  • Optimal tuning parameters for minimizing MSE are identified.
  • Theoretical convergence properties of the estimator are established.

Conclusions:

  • The proposed kNN-based estimator offers a robust method for estimating divergence measures.
  • The derived theoretical properties provide a basis for performance analysis in related fields.
  • This work facilitates the development and evaluation of advanced image registration algorithms.