Bøger i Springer Series in Perception Engineering serien

He studied issues re lated to characterization of surfaces in the context of object recognition, and then uses the features thus developed for recognizing objects. He uses a multi-view representation of 3-D objects for recognition, and he devel ops techniques for the segmentation of range images to obtain features for recognition.

It is still the case, however, that the perfor of computer vision systems falls far short of that of the natural systems mance they are intended to mimic, suggesting that it is time to look even more closely at the remaining differences between artificial and biological vision systems.

Machine Vision technology is becoming an indispensible part of the manufacturing industry. Biomedical and scientific applications of machine vision and imaging are becoming more and more sophisticated, and new applications continue to emerge.

This book addresses an area of perception engineering which deals with constructive processes. As the field of perception engineering seems to be of growing scientific and applied importance, both practitioners and researchers in machine perception will find this book a valuable addition to their libraries.

The symbolic de- scription scheme consists of a novel taxonomy for textures, and is based on appropriate mathematical models for different kinds of texture. Disordered textures are described by statistical mea- sures, strongly ordered textures by the placement of primitives, and weakly ordered textures by an orientation field.

Much of our understanding of the relationships among geometric struc tures in images is based on the shape of these structures and their relative orientations, positions and sizes. Thus, developing quantitative methods for capturing shape information from digital images is an important area for computer vision research.

Skifstad's dissertation proposes a new approach to recover depth information using known camera motion. A very interesting aspect of the approach pursued by Skifstad is the method used to bypass the most difficult and computationally expensive step in using stereo or similar approaches for the vision-based depth esti mation.

Computer vision researchers have been frustrated in their attempts to automatically derive depth information from conventional two-dimensional intensity images.

Its aims are to develop a theoretical framework, devise appropriate algorithms, and demonstrate a software implementation of those algorithms that will confirm the usefulness of surfaces in range image understanding.

It is still the case, however, that the perfor of computer vision systems falls far short of that of the natural systems mance they are intended to mimic, suggesting that it is time to look even more closely at the remaining differences between artificial and biological vision systems.

Machine Vision technology is becoming an indispensible part of the manufacturing industry. Biomedical and scientific applications of machine vision and imaging are becoming more and more sophisticated, and new applications continue to emerge.

Since the early days of computers, machine learning and automatic programming have attracted researchers in computer science and related fields, particularly pattern recognition and automatic control theory.

Rather than attempting to construct highly specialized algorithms for recognizing physical objects, as is customary in modern computer vision research, the application and subsequent evaluation of large numbers of relatively straightforward image processing routines is used to recognize natural features such as trees, bushes, and rocks.

There is a growing social interest in developing vision-based vehicle guidance systems for improving traffic safety and efficiency and the environment. Ex- amples of vision-based vehicle guidance systems include collision warning systems, steering control systems for tracking painted lane marks, and speed control systems for preventing rear-end collisions. Like other guidance systems for aircraft and trains, these systems are ex- pected to increase traffic safety significantly. For example, safety improve- ments of aircraft landing processes after the introduction of automatic guidance systems have been reported to be 100 times better than prior to installment. Although the safety of human lives is beyond price, the cost for automatic guidance could be compensated by decreased insurance costs. It is becoming more important to increase traffic safety by decreasing the human driver's load in our society, especially with an increasing population of senior people who continue to drive. The second potential social benefit is the improvement of traffic efficiency by decreasing the spacing between vehicles without sacrificing safety. It is reported, for example, that four times the efficiency is expected if the spacing between cars is controlled automatically at 90 cm with a speed of 100 kmjh compared to today's typical manual driving. Although there are a lot of tech- nical, psychological, and social issues to be solved before realizing the high- density jhigh-speed traffic systems described here, highly efficient highways are becoming more important because of increasing traffic congestion.