Multiframe Shift Estimation

Abstract

The purpose of this research was to develop a fundamental framework for a new approach to multiframe translational shift estimation in image processing. This thesis sought to create a new multiframe shift estimator, to theoretically prove and experimentally test key properties of it, and to quantify its performance according to several metrics. The new estimator was modeled successfully and was proven to be an unbiased estimator under certain common image noise conditions. Furthermore its performance was shown to be superior to the cross correlation shift estimator, a robust estimator widely used in similar image processing cases, according to several criteria. This research effort led to the derivation of a lower bound of estimation performance for the multiframe case. This valuable data analysis tool extends current boundary derivations to include prior information about the random shifting, thereby providing a more precise performance boundary.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2004
Accession Number
ADA455828

Entities

People

  • Stephen A. Bruckart

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Boundaries
  • Charge Coupled Devices
  • Cross Correlation
  • Data Analysis
  • Data Science
  • Digital Images
  • Electrical Engineering
  • Estimators
  • Gaussian Distributions
  • Image Processing
  • Information Processing
  • Information Science
  • Mathematical Models
  • Random Variables
  • Statistical Algorithms

Readers

  • Computer Vision.
  • Statistical inference.
  • Systems Analysis and Design