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Time-recursive velocity-adapted spatio-temporal scale-space filters
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-9081-2170
2002 (English)In: : ECCV'02 published in Springer Lecture Notes in Computer Science, volume 2350, 2002, Vol. 2350, 52-67 p.Conference paper (Refereed)
Abstract [en]

This paper presents a theory for constructing and computing velocity-adapted scale-space filters for spatio-temporal image data. Starting from basic criteria in terms of time-causality, time-recursivity, locality and adaptivity with respect to motion estimates, a family of spatio-temporal recursive filters is proposed and analysed. An important property of the proposed family of smoothing kernels is that the spatio-temporal covariance matrices of the discrete kernels obey similar transformation properties under Galilean transformations as for continuous smoothing kernels on continuous domains. Moreover, the proposed theory provides an efficient way to compute and generate nonseparable scale-space representations without need for explicit external warping mechanisms or keeping extended temporal buffers of the past. The approach can thus be seen as a natural extension of recursive scale-space filters from pure temporal data to spatio-temporal domains.

Place, publisher, year, edition, pages
2002. Vol. 2350, 52-67 p.
National Category
Computer Science Computer Vision and Robotics (Autonomous Systems) Mathematics
URN: urn:nbn:se:kth:diva-40201DOI: 10.1007/3-540-47969-4_4OAI: diva2:442098
7th European Conference on Computer Vision

QC 20110920

Available from: 2013-04-05 Created: 2011-09-13 Last updated: 2013-04-05Bibliographically approved

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