Template Matching: Matched Spatial Filters and Beyond.

Abstract

Template matching by means of cross-correlation is common practice in pattern recognition. However, its sensitivity to deformations of the pattern and the broad and unsharp peaks it produces are significant drawbacks. This paper reviews some results on how these shortcomings can be removed. Several techniques (Matched Spatial Filters, Synthetic Discriminant Functions, Principal Components Projections and Reconstruction Residuals) are reviewed and compared on a common task: locating eyes in a database of faces. New variants are also proposed and compared: least squares Discriminant Functions and the combined use of projections on eigenfunctions and the corresponding reconstruction residuals. Finally, approximation networks are introduced in an attempt to improve filter design by the introduction of nonlinearity.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1995
Accession Number
ADA318849

Entities

People

  • Roberto Brunelli
  • Tomaso Poggio

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

  • Algebra
  • Correlation Techniques
  • Cross Correlation
  • Data Science
  • Databases
  • Eigenvectors
  • Identification
  • Information Science
  • Mathematics
  • Pattern Recognition
  • Recognition
  • Residuals
  • Sensitivity
  • Template Patterns

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms