Landmark-Based Image Analysis

1. Front Matter

   Pages i-xiii

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2. Introduction and Overview

   • Karl Rohr

   Pages 1-34

3. Detection and Localization of Point Landmarks

   • Karl Rohr

   Pages 35-108

4. Performance Characterization of Landmark Operators

   • Karl Rohr

   Pages 109-177

5. Elastic Registration of Multimodality Images

   • Karl Rohr

   Pages 179-258

6. Back Matter

   Pages 259-305

   PDF

Landmarks are preferred image features for a variety of computer vision tasks such as image mensuration, registration, camera calibration, motion analysis, 3D scene reconstruction, and object recognition. Main advantages of using landmarks are robustness w. r. t. lightning conditions and other radiometric vari­ ations as well as the ability to cope with large displacements in registration or motion analysis tasks. Also, landmark-based approaches are in general com­ putationally efficient, particularly when using point landmarks. Note, that the term landmark comprises both artificial and natural landmarks. Examples are comers or other characteristic points in video images, ground control points in aerial images, anatomical landmarks in medical images, prominent facial points used for biometric verification, markers at human joints used for motion capture in virtual reality applications, or in- and outdoor landmarks used for autonomous navigation of robots. This book covers the extraction oflandmarks from images as well as the use of these features for elastic image registration. Our emphasis is onmodel-based approaches, i. e. on the use of explicitly represented knowledge in image analy­ sis. We principally distinguish between geometric models describing the shape of objects (typically their contours) and intensity models, which directly repre­ sent the image intensities, i. e. ,the appearance of objects. Based on these classes of models we develop algorithms and methods for analyzing multimodality im­ ages such as traditional 20 video images or 3D medical tomographic images.

• artificial intelligence
• computer
• computer graphics
• computer vision
• detection
• image analysis
• image registration
• neurosurgery
• performance
• radiology
• remote sensing
• thoracic surgery

• Department of Computer Science, University of Hamburg, Hamburg, Germany

  Karl Rohr

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