Abstract

Automated tracking and analysis of moving objects in image sequences has been and continues to be one of the major themes in digital image analysis research. This is not surprising in view of its many applications in video surveillance, multimedia services, automated vehicle guidance and driver assistance, remote sensing and meteorology, and medical imaging. It is also a recurring theme in molecular biology. By their very nature, biomolecular systems are dynamic, and it is one of the major challenges of biomedical research and pharmaceutical industries in the postgenomic era to unravel the spatial and temporal relationships of these complex systems and to devise strategies to manipulate them. Results in this area can be expected to have profound social and economic impact in the near future, as they can be harnessed to improve human health and well-being [1]. Studies into biomolecular dynamics generate ever increasing amounts of image data. To be able to handle these data and to fully exploit them for describing biological processes on a quantitative level and building accurate mathematical models of dynamic structures, computerized motion analysis is rapidly becoming a requisite [2]. Over the past decades, a number of image analysis techniques have been developed in support of such studies, the details of which were often buried in the small print of the methods sections of papers published in the biology and biophysics literature. The majority of these techniques were based on rather rudimentary

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