Bøger i Advances in Computer Vision and Machine Intelligence serien

It has long been a dream to realize machines with flexible visual perception capability. Research on digital image processing by computers was initiated about 30 years ago, and since then a wide variety of image processing algorithms have been devised. Using such image processing algorithms and advanced hardware technologies, many practical ma­ chines with visual recognition capability have been implemented and are used in various fields: optical character readers and design chart readers in offices, position-sensing and inspection systems in factories, computer tomography and medical X-ray and microscope examination systems in hospitals, and so on. Although these machines are useful for specific tasks, their capabilities are limited. That is, they can analyze only simple images which are recorded under very carefully adjusted photographic conditions: objects to be recognized are isolated against a uniform background and under well-controlled artificial lighting. In the late 1970s, many image understanding systems were de­ veloped to study the automatic interpretation of complex natural scenes. They introduced artificial intelligence techniques to represent the knowl­ edge about scenes and to realize flexible control structures. The first author developed an automatic aerial photograph interpretation system based on the blackboard model (Naga1980). Although these systems could analyze fairly complex scenes, their capabilities were still limited; the types of recognizable objects were limited and various recognition vii viii Preface errors occurred due to noise and the imperfection of segmentation algorithms.

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". First of all, it can be stated that the image does not show an everyday scene. The image shows a snapshot of an assembly line.

Irrespective of the necessities described above, and with the excep tion of specific generic application systems (e.g., bare-board PCB inspection, wafer inspection, solder joint inspection, linewidth measure ment), vision systems are still not found frequently in today's electronics factories.

In this groundbreaking new volume, computer researchers discuss the development of technologies and specific systems that can interpret data with respect to domain knowledge. Although the chapters each illuminate different aspects of image interpretation, all utilize a common approach - one that asserts such interpretation must involve perceptual learning in terms of automated knowledge acquisition and application, as well as feedback and consistency checks between encoding, feature extraction, and the known knowledge structures in a given application domain. The text is profusely illustrated with numerous figures and tables to reinforce the concepts discussed.

Image motion processing is important to machine vision systems because it can lead to the recovery of 3D structure and motion. In addition, the author considers interpretation by knowledge-based systems and presents the relevant mathematical basis for 3D interpretation.

Computer vision deals with the problem of manipulating information contained in large quantities of sensory data, where raw data emerge from the transducing 6 7 sensors at rates between 10 to 10 pixels per second.

Image motion processing is important to machine vision systems because it can lead to the recovery of 3D structure and motion. In addition, the author considers interpretation by knowledge-based systems and presents the relevant mathematical basis for 3D interpretation.

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". First of all, it can be stated that the image does not show an everyday scene. The image shows a snapshot of an assembly line.

Afterwards, different model ing approaches are addressed in six chapters contributed by major research groups in the field: modeling based on object shape is addressed in chapter 2 by F.