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- W1936181330 abstract "This chapter presents an analytical, numerical, and experimental study of the performance of low-level motion estimators in spatiotemporal images. Motivation for this work arose from scientific applications of image sequence processing within the frame of an interdisciplinary research unit. Here, the study of transport, exchange, and growth processes with various imaging techniques requires highly accurate velocity estimates (Jähne et al., 1996; Jähne et al., 1998). These high accuracy demands triggered a revisit of the fundamentals of motion estimation in spatiotemporal images. In this chapter only low-level motion estimators are discussed. This is only a part of the picture, but errors in low-level estimators propagate and thus cause also errors in higher-level features. Only a few systematic studies of the performance characteristics of low-level motion estimators are available in the literature. Kearney et al. (1987) performed an error analysis of optical flow estimation with gradient-based methods, while Simoncelli (1999) studied the error propagation of multi-scale differential optical flow. Barron et al. (1994) used a set of computer-generated and real image sequence to compare various approaches to optical flow computation. To study the performance of phase-based and energy-based techniques, Haglund and Fleet (1994) used an image sequence generated by warping a single natural image. Otte and Nagel (1994) were the first to verify motion estimators with a calibrated real-world sequence. Bainbridge-Smith and Lane (1997) theoretically compared various first and second-order differential techniques and proved the results using a series of test sequences." @default.
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- W1936181330 date "2000-01-01" @default.
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- W1936181330 title "Performance Characteristics of Low-Level Motion Estimators in Spatiotemporal Images" @default.
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- W1936181330 doi "https://doi.org/10.1007/978-94-015-9538-4_12" @default.
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