Local Rotational Symmetries.

Abstract

This theis describes a new representation for two-dimensional round regions called Local Rotational Symmetries. Local Rotational Symmetries are intended as a companion to Brady's Smoothed Local Symmetry Representation for elongated shapes. An algorithm for computing Local Rotational Symmetry representations at multiple scales of resolution has been implemented and results of this implementationnare presented. These results suggest that Local Rotational Symmetries provide a more robustly computable and perceptually accurate description of round regions than previous proposed representations. In the course of developing this representation, it has been necessary to modify the way both Smoothed Local Symmetries and Local Rotational Symmetries are computed. First, grey-scale image smoothing proves to be better than boundary smoothing for creating representations at multiple scales of resolution, because it is more robust and it allows qualitative changes in representations between scales. Secondly, it is proposed that shape representations at different scales of resolution be explicitly related, so that information can be passed between scales and computation at each scale can be kept local. Such a model for multi-scale computation is desirable both to allow efficient computation and to accurately model human perceptions. Additional keywords: Image understanding; Computer vision; Artificial intelligence; Shape representation; Computer graphics.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1985
Accession Number
ADA159522

Entities

People

  • M. M. Fleck

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Biomedical

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Computational Science
  • Computations
  • Computer Graphics
  • Computer Languages
  • Computer Science
  • Computer Vision
  • Geometric Forms
  • Image Processing
  • Language
  • Lines (Geometry)
  • Pattern Recognition
  • Psychology
  • Three Dimensional
  • Two Dimensional

Readers

  • Computer Vision.

Technology Areas

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