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14 Motion (p. 397-398)
Motion analysis long used to be a specialized research area that had not much to do with general image processing. This separation had two reasons. First, the techniques used to analyze motion in image sequences were quite different. Second, the large amount of storage space and computing power required to process image sequences made image sequence analysis available only to a few specialized institutions that could afford to buy the expensive specialized equipment.
Both reasons are no longer true. Because of the general progress in image processing, the more advanced methods used in motion analysis no longer differ from those used for other image processing tasks. The rapid progress in computer hardware and algorithms makes the analysis of image sequences now feasible even on standard personal computers and workstations.
Therefore we treat motion in this chapter as just another feature that can be used to identify, characterize, and distinguish objects and to understand scenes. Motion is indeed a powerful feature. We may compare the integration of motion analysis into mainstream image processing with the transition from still photography to motion pictures.
Only image sequence analysis allows us to recognize and analyze dynamic processes. Thus far-reaching capabilities become available for scientific and engineering applications including the study of flow; transport; biological growth processes from the molecular to the ecosystem level; diurnal, annual, and interannual variations; industrial processes; trafic; autonomous vehicles and robots - to name just a few application areas. In short, everything that causes temporal changes or makes them visible in our world is a potential subject for image sequence analysis.
The analysis of motion is still a challenging task and requires some special knowledge. Therefore we discuss the basic problems and principles of motion analysis in Section 14.2. Then we turn to the various techniques for motion determination. As in many other areas of image processing, the literature is swamped with a multitude of approaches. This book should not add to the confusion. We emphasize instead the basic principles and we try to present the various concepts in a unified way as filter operations on the space-time images.
In this way, the interrelations between the different concepts are made transparent. In this sense, we will discuss differential (Section 14.3), tensor (Section 14.4), correlation (Section 14.5), and phase (Section 14.6) techniques as elementary motion estimators.