Digital Image Analysis

1. Front Matter

   Pages i-xxx

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2. Mathematical Methods for Image Analysis

   1. Front Matter

      Pages 1-1

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   2. Introduction to Part I

      Pages 3-5

   3. Numerical Harmonic Analysis and Image Processing

      • Hans G. Feichtinger, Thomas Strohmer

      Pages 7-47

   4. Stochastic Shape Theory

      • Christian Cenker, Georg Pflug, Manfred Mayer

      Pages 49-79

   5. Image Compression and Coding

      • Josef Scharinger

      Pages 81-114

3. Back Matter

   Pages 115-130

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4. Data Handling

   1. Front Matter

      Pages 131-131

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   2. Introduction to Part II

      Pages 133-133

   3. Parallel and Distributed Processing

      • Alois Goller, Ian Glendinning, Dieter Bachmann, Rainer Kalliany

      Pages 135-153

   4. Image Data Catalogs

      • Franz Niederl, Rainer Kalliany, Caterina Saraceno, Walter G. Kropatsch

      Pages 155-169

5. Back Matter

   Pages 171-174

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6. Robust and Adaptive Image Understanding

   1. Front Matter

      Pages 175-175

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   2. Introduction to Part III

      Pages 177-178

   3. Graphs in Image Analysis

      • Walter G. Kropatsch, Mark Burge, Roland Glantz

      Pages 179-197

   4. Hierarchies

      • Walter G. Kropatsch, Horst Bischof, Roman Englert

      Pages 199-218

   5. Robust Methods

      • Aleš Leonardis, Horst Bischof

      Pages 219-235

   6. Structural Object Recognition

      • Mark Burge, Wilhelm Burger

      Pages 237-249

   7. Machine Learning

      • Edward Blurock

      Pages 251-263

7. Back Matter

   Pages 265-280

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The human visual system as a functional unit including the eyes, the nervous system, and the corresponding parts of the brain certainly ranks among the most important means of human information processing. The e?ciency of the biological systems is beyond the capabilities of today’s technical systems, even with the fastest available computer systems. However, there are areas of application where digital image analysis systems produce acceptable results. Systems in these areas solve very specialized tasks, they operate in a limited environment, and high speed is often not necessary. Several factors determine the economical application of technical vision systems: cost, speed, ?exibility, robu- ness, functionality, and integration with other system components. Many of the recent developments in digital image processing and pattern recognition show some of the required achievements. Computer vision enhances the capabilities of computer systems • in autonomously collecting large amounts of data, • in extracting relevant information, • in perceiving its environment, and • in automatic or semiautomatic operation in this environment. The development of computer systems in general shows a steadily increasing need in computational power, which comes with decreasing hardware costs.

• 3D
• Augmented Reality
• Digital Imaging
• calibration
• image analysis
• image compression
• image processing
• image understanding
• imaging
• object recognition
• pattern recognition
• remote sensing

• Pattern Recognition and Image Processing Group, Institute of Computer Aided Automation, Vienna University of Technology, Vienna, Austria

  Walter G. Kropatsch, Horst Bischof

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