Fundamentals of Machine Vision

  • Christoph Stiller
  • Alexander Bachmann
  • Andreas Geiger
Reference work entry

Abstract

Automobiles may acquire a rich variety of relevant information from image data and its analysis using machine vision techniques. This chapter provides an overview on the principles underlying image formation and image analysis. The perspective projection model is formulated to describe the mapping of the 3D real world onto the 2D image plane with its intrinsic and extrinsic calibration parameters. Image analysis typically begins with the identification of features. These may describe locations of particular local intensity patterns in a single image, such as edges or corners, or may quantify the 2D displacement of corresponding pixels between two images acquired at different time instances or by a multicamera system. Such features can be used to reconstruct the 3D geometry of the real world using stereo vision, motion stereo, or multiview reconstruction. Temporal tracking using Bayesian filters and its variations not only improves accuracy but readily allows for information fusion with data of other sensors. The chapter closes with two application examples. The first addresses object detection and tracking using multiple image features. The second application focuses on intersection understanding illustrating the large potential of high-level scene interpretation through machine vision.

Notes

Acknowledgments

The authors thank Dr. Christian Duchow for valuable contributions to an early version of this chapter.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Christoph Stiller
    • 1
  • Alexander Bachmann
    • 2
  • Andreas Geiger
    • 3
  1. 1.Institut für Mess- und RegelungstechnikKarlsruher Institut für Technologie (KIT)KarlsruheGermany
  2. 2.ADC Automotive Distance Control Systems GmbHLindauGermany
  3. 3.MPI for Intelligent SystemsTübingenGermany

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