Vision-based terrain learning

Abstract

This paper presents an algorithm for online image-based terrain classification that mimics a human supervisor s segmentation and classification of training images into Go and NoGo regions. The algorithm identifies a set of image chips (or exemplars) in the training images that span the range of terrain appearance. It then uses the exemplars to segment novel images and assign a Go/NoGo classification. System parameters adapt to new inputs, providing a mechanism for learning. System performance is compared to that obtained via offline fuzzy c-means clustering and support vector machine classification.

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Document Details

Document Type
Technical Report
Publication Date
Apr 17, 2006
Accession Number
ADA594305

Entities

People

  • Gary Witus
  • Robert Karlsen

Organizations

  • United States Army Tank Automotive Research, Development and Engineering Center

Tags

Communities of Interest

  • Air Platforms
  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Autonomous Navigation
  • Cameras
  • Clustering
  • Coding
  • Computational Science
  • Computer Vision
  • Coordinate Systems
  • Information Science
  • Kernel Functions
  • Learning
  • Machine Learning
  • Robot Navigation
  • Robots
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science

Readers

  • Computer Vision.

Technology Areas

  • AI & ML