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Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes
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Multiple resolution representation and probabilistic matching of 2-d gray-scale shape.

J L Crowley1, A C Sanderson

  • 1LIFIA (IMAG), BP 68, 38402 St.-Martin-d'Heres, France; Robotics Institute, Carnegie-Mellon University, Pittsburgh, PA 15213.

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Summary

This study introduces a novel probabilistic representation for structural pattern classification. It enables more accurate symbol correspondence in pattern matching using multi-resolution grayscale information and connectivity constraints.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Structural pattern classification relies on matching pattern descriptions to class models.
  • Determining symbol correspondence between patterns and models is a central challenge.
  • Pattern representation significantly impacts the difficulty of correspondence matching.

Purpose of the Study:

  • To present a probabilistic representation for structural models in pattern classification.
  • To develop an algorithm for determining symbol correspondence in structural pattern matching.
  • To introduce an interactive training program for learning pattern class models.

Main Methods:

  • Utilizing a tree-based representation for both pattern descriptions and structural models.
  • Employing symbols representing multi-resolution grayscale information with attribute values.
  • Developing a correspondence algorithm that leverages inter-scale symbol connectivity to constrain search.

Main Results:

  • Demonstrated a probabilistic framework for structural pattern models.
  • Presented an algorithm for efficient symbol correspondence determination.
  • Introduced an interactive system for model training.

Conclusions:

  • The proposed probabilistic representation enhances structural pattern matching.
  • The algorithm effectively uses multi-scale connectivity for improved correspondence.
  • The interactive training program facilitates model learning for pattern classes.