Research in Knowledge-Based Automatic Feature Extraction

Abstract

This report provides a description of USC's activities as part of the DARPA Automated Population of Geospatial Databases (APGD) project. Focus of this effort is on detection, delineation, and description of 3-D buildings from aerial images. Buildings are objects of obvious importance in urban environments and accurate models of them are needed for a number of tasks such as mission planning, mission rehearsal, tactical training, damage assessment, and change detection. Difficulties in modeling buildings come from problems of segmentation, 3-D inference, and shape description. Development of a multi-image based system is described. This system operates on a hypothesize and verify paradigm. Hypotheses for rectilinear rooftops are generated by hierarchical grouping of lower level features. Three dimensional cues from walls and shadows are used to verify and select from these hypotheses. Three-dimensional cues from range sensors, such as IFSAR, can be used when available. Results on selected windows of Fort Hood, TX, and Fort Benning, GA, sites are presented. An efficient interactive system can be used to edit the results of the automatic system to reduce the effort required from the user. The current system is limited to rectilinear shaped buildings and assumes that ground height, camera models, and illumination parameters are known.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1999
Accession Number
ADA364229

Entities

People

  • A. Huertas
  • R. Nevatia

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Sensors

DTIC Thesaurus Topics

  • Automatic
  • Bayesian Networks
  • Computer Vision
  • Control Systems
  • Data Sets
  • Detection
  • Detectors
  • Engineering
  • Extraction
  • False Alarms
  • Feature Extraction
  • Geometry
  • Machine Learning
  • Probability
  • Probability Distributions
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Computer Vision.
  • Energy Conservation and Renewable Energy Engineering.

Technology Areas

  • AI & ML