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Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo

Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo

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Original languageEnglish
Title of host publication2007 IEEE Workshop on Motion and Video Computing, WMVC 2007
PublisherIEEE Computer Society
ISBN (Electronic)9780769527932
DOIs
StatePublished - 2007
Event2007 IEEE Workshop on Motion and Video Computing - Austin, United States

Workshop

Workshop2007 IEEE Workshop on Motion and Video Computing
CountryUnited States
CityAustin
Period23/02/0724/02/07

Abstract

The performance of various low-level motion estimation methods applied to fluorescence labelled growing cellular structures imaged using confocal laser scanning microscopy is investigated. This is a challenging and unusual domain for motion estimation methods. A selection of methods are discussed that can be contrasted in terms of how much spatial or temporal contextual information is used. The Lucas Kanadì feature tracker, a spatially and temporally localised method, was, as one would expect, accurate around resolvable structure. It was not able to track the smaller, repetitive cell structure in the root tip and was somewhat prone to identifying spurious features. This approach is improved by developing a full multi-frame, robust, Bayesian method, and it is demonstrated that by using extra frames with motion constraints reduces such errors. Next, spatially global methods are discussed, including robust variational smoothing and Markov Random Field (MRF) modelling. A key conclusion that is drawn from investigation of these methods is that generic low-level (robust) smoothing functions do not provide good results in this application and that this is probably due to the large regions with little stable structure. Furthermore, contrary to recently reported successes, graph cuts and loopy belief propagation for MAP estimation of the MRF labels provided often poor and inconsistent estimates. The results suggest the need for greater emphasis on temporal smoothing for generic low-level motion estimation tools and more task specific, spatial constraints, perhaps in the form of high level models in order to accurately recover motion from such data. Finally, the form of the estimated growth is briefly discussed and related to contemporary biological models. We hope that this paper will assist non-specialists in applying state-of-the-art methods to this form of data.

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