Traditionally, say 15 years ago, three-dimensional image analysis (aka computer vi sion) and three-dimensional image synthesis (aka computer graphics) were separate fields. Rarely were expert"

Traditionally, three-dimensional image analysis (a.k.a. computer vision) and three-dimensional image synthesis (a.k.a. computer graphics) were separate fields. Rarely were experts working in one area interested in and aware of the advances in the other. Over the last decade this has changed dramatically, reflecting the growing maturity of each of these areas. The vision and graphics communities are today engaged in a mutually beneficial exchange, learning from each other and coming up with new ideas and techniques that build on the state of the art in both fields. This book is the result of a fruitful collaboration between scientists at the University of NA1/4rnberg, Germany, who, coming from diverse fields, are working together propelled by the vision of a unified area of three-dimensional image analysis and synthesis. Principles of 3D Image Analysis and Synthesis starts out at the image acquisition end of a hypothetical processing chain, proceeds with analysis, recognition and interpretation of images, towards the representation of scenes by 3D geometry, then back to images via rendering and visualization techniques. Coverage includes discussion of range cameras, multiview image processing, the structure-from-motion problem, object recognition, knowledge-based image analysis, active vision, geometric modeling with meshes and splines, and reverse engineering. Also included is cutting-edge coverage of texturing techniques, global illumination, image-based rendering, volume visualization, flow visualization techniques, and acoustical imaging including object localization from audio and video. This state-of-the-art volume is a concise and readable reference for scientists, engineers, graduate students and educators working in image processing, vision, computer graphics, or visualization.

In this second edition every chapter of the first edition of Pattern Analysis has been updated and expanded. The general view of a system for pattern analysis and understanding has remained unchanged, but many details have been revised. A short account of light and sound has been added to the introduction, some normalization techniques and a basic introduction to morphological operations have been added to the second chapter. Chapter 3 has been expanded significantly by topics like motion, depth, and shape from shading; additional material has also been added to the already existing sections of this chapter. The old sections of Chap. 4 have been reorganized, a general view of the classification problem has been added and material provided to incorporate techniques of word and object recognition and to give a short account of some types of neural nets. Almost no changes have been made in Chap. 5. The part on representation of control structures in Chap. 6 has been shortened, a section on the judgement of results has been added. Chapter 7 has been rewritten almost completely; the section on formal grammars has been reduced, the sections on production systems, semantic networks, and knowledge acquisition have been expanded, and sections on logic and explanation added. The old Chaps. 8 and 9 have been omitted. In summary, the new edition is a thorough revision and extensive update of the first one taking into account the progress in the field during recent years.

Figure 1.1. An outdoor scene "A bus is passing three cars which are parking between trees at the side of the road. Houses having two storeys are lined up at the street. 3 4 Introduction Figure 1.2. An assembly scene There seems to be a small open place between the group of houses in the foreground and the store in the background". In such or a similar way the content of the natural scene shown above can be described. It is quite easy to give such a short description. The problem is somewhat more complex for the second image. First of all, it can be stated that the image does not show an everyday scene. It appears as a kind of man made surrounding. But everyone can accept the following statements about this image: 1. The image shows a snapshot of an assembly line. 2. The robot in front is screwing. 3. There is no person in the working area of the robots. 4. All objects on the conveyor belt are worked on by robots. There are no free objects on the belt.

Figure 1.1. An outdoor scene "A bus is passing three cars which are parking between trees at the side of the road. Houses having two storeys are lined up at the street. 3 4 Introduction Figure 1.2. An assembly scene There seems to be a small open place between the group of houses in the foreground and the store in the background." In such or a similar way the content of the natural scene shown above can be described. It is quite easy to give such a short description. The problem is somewhat more complex for the second image. First of all, it can be stated that the image does not show an everyday scene. It appears as a kind of man made surrounding. But everyone can accept the following statements about this image: 1. The image shows a snapshot of an assembly line. 2. The robot in front is screwing. 3. There is no person in the working area of the robots. 4. All objects on the conveyor belt are worked on by robots. There are no free objects on the belt.

Der vorliegende Band behandelt Verfahren der Kunstlichen Intelligenz (KI) in der Bild- und Sprachanalyse, also in einem Teilgebiet der Mustererkennung (ME). Die Definition und Abgrenzung von Begriffen wie KI und ME wird in der Literatur nicht einheitl ich gehandhabt; es ist aber wichtig daran zu erinnern, dass beide aus ihrer Fruhzeit gemeinsame Wurzeln haben. Die Fahigkeit zur Erkennung von Mustern, und ganz allgemein zur Wahrnehmung der Umwelt mit geeigneten Sensoren, wurde als wesentl i che Vorau ssetzung fur autonom agi erende "i nte 11 i gente" tech nische Systeme angesehen. Einerseits wurde in ersten Veroffentlichungen uber KI immer wieder die ME als ein zu losendes Problem genannt, andererseits wurde als wichtiges Problem in der ME die Einbeziehung von Wissen uber das Problem und die zu erkennenden Muster gefordert. Die Wichtigkeit der Wissensverarbeitung in der ME lasst sich in Forschungsantragen schon aus dem Ende der funfziger Jahre nach lesen. Zu dieser Zeit war der Stand der ME naturl ich noch nicht so weit ent wickelt wie heute, er stand praktisch auf der Stufe der Extraktion und Klassifi kation von Merkmalvektoren. Leider wird immer noch vielfach ME mit diesem An fangsstand der Technik verwechselt und nicht die generelle Aufgabe, namlich die automatische Verarbei tung, Auswertung und Interpretation sensorischer Informa tion gesehen. Inzwi schen sind auf dem Sektor der Wi ssensverarbeitung in der KI solche Fortschritte erzielt worden, dass man in der ME die Nutzung von Wi ssen nicht mehr nur fordern sondern tatsachl ich auch durchfuhren kann."