Geometric Constraints for Object Detection and Delineation

  • Jefferey¬†Shufelt

Table of contents

  1. Front Matter
    Pages i-x
  2. Jefferey Shufelt
    Pages 1-19
  3. Jefferey Shufelt
    Pages 21-39
  4. Jefferey Shufelt
    Pages 41-73
  5. Jefferey Shufelt
    Pages 75-109
  6. Jefferey Shufelt
    Pages 111-141
  7. Jefferey Shufelt
    Pages 143-191
  8. Jefferey Shufelt
    Pages 193-199
  9. Back Matter
    Pages 201-265

About this book

Introduction

The ability to extract generic 3D objects from images is a crucial step towards automation of a variety of problems in cartographic database compilation, industrial inspection and assembly, and autonomous navigation. Many of these problem domains do not have strong constraints on object shape or scene content, presenting serious obstacles for the development of robust object detection and delineation techniques. Geometric Constraints for Object Detection and Delineation addresses these problems with a suite of novel methods and techniques for detecting and delineating generic objects in images of complex scenes, and applies them to the specific task of building detection and delineation from monocular aerial imagery.
PIVOT, the fully automated system implementing these techniques, is quantitatively evaluated on 83 images covering 18 test scenes, and compared to three existing systems for building extraction. The results highlight the performance improvements possible with rigorous photogrammetric camera modeling, primitive-based object representations, and geometric constraints derived from their combination. PIVOT's performance illustrates the implications of a clearly articulated set of philosophical principles, taking a significant step towards automatic detection and delineation of 3D objects in real-world environments.
Geometric Constraints for Object Detection and Delineation is suitable as a textbook or as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.

Keywords

3D Navigation automation autonom database digital elevation model modeling performance

Authors and affiliations

  • Jefferey¬†Shufelt
    • 1
  1. 1.Carnegie Mellon UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-5273-4
  • Copyright Information Kluwer Academic Publishers 2000
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7405-3
  • Online ISBN 978-1-4615-5273-4
  • Series Print ISSN 0893-3405
  • About this book