Outdoor Landmark Recognition Using Hybrid Fractal Vision System and Neural Networks

Abstract

Landmarks are useful cues for autonomous mobile robot navigation. Due to the changing imaging conditions, the appearance of the landmarks is varying in outdoor environments. We will develop a novel system to detect and recognize the landmarks. The developed system will be able to overcome changes in scale, lighting, etc. The proposed approach is based on a two-step method, using both fractal based object classifier and neural network based object identifier. Since fractals are inherently scale invariant over a finite range of scales, they make good models for outdoor scene objects. Since neural networks have fast recognition capabilities, they are a good choice in real time mobile robot applications.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1994
Accession Number
ADA282506

Entities

Organizations

  • North Carolina State University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Autocorrelation
  • Autonomous Navigation
  • Brownian Motion
  • Contractors
  • Guidance
  • Information Science
  • Intensity
  • Mathematical Models
  • Models
  • Navigation
  • Neural Networks
  • Recognition
  • Robot Navigation
  • Robots
  • Self Organizing Systems
  • Statistical Analysis

Fields of Study

  • Computer science

Readers

  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks
  • Autonomy
  • Autonomy - Autonomous System Control