Scale-space with causal time direction
1996 (English)In: : ECCV'96 (Cambridge, U.K.) published in Springer Lecture Notes in Computer Science, vol 1064, Berlin / Heidelberg: Springer , 1996, Vol. 1064, 229-240 p.Conference paper (Refereed)
This article presents a theory for multi-scale representation of temporal data. Assuming that a real-time vision system should represent the incoming data at different time scales, an additional causality constraint arises compared to traditional scale-space theory—we can only use what has occurred in the past for computing representations at coarser time scales. Based on a previously developed scale-space theory in terms of noncreation of local maxima with increasing scale, a complete classification is given of the scale-space kernels that satisfy this property of non-creation of structure and respect the time direction as causal. It is shown that the cases of continuous and discrete time are inherently different.
Place, publisher, year, edition, pages
Berlin / Heidelberg: Springer , 1996. Vol. 1064, 229-240 p.
, Lecture Notes in Computer Science, 1064
scale-space, time, scale, motion, causality, Poisson kernel, Gaussian kernel, smoothing, visual front-end, multi-scale representation, computer vision, signal processing
Computer and Information Science Computer Vision and Robotics (Autonomous Systems) Signal Processing
IdentifiersURN: urn:nbn:se:kth:diva-33682DOI: 10.1007/BFb0015539OAI: oai:DiVA.org:kth-33682DiVA: diva2:417090
4th European Conference on Computer Vision, (Cambridge, England), April 14-18, 1996
QC 201304052013-04-052011-05-152013-04-05Bibliographically approved