2015/01/01 by Tony Lindeberg
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Cascade #Domain (mathematical analysis) #Exponential function #Gaussian #Maxima and minima #Model Reduction and Neural Networks #Neural Networks and Reservoir Computing #Nonlinear Dynamics and Pattern Formation #Receptive field #Scale (ratio) #Separable space #cs.CV #q-bio.NC
paper · pdf · doi:10.1007/978-3-319-18461-6_8
published as Proc SSVM 2015: Scale-Space and Variational Methods for Computer Vision, Springer LNCS vol 9087, pages 90-102, 2015 · 12 pages, 2 figures, 2 tables. arXiv admin note: substantial text overlap with arXiv:1404.2037
openalex publication_date 2015/01/01 · arxiv created 2015/04/07 · openalex created_date 2016/06/24 · arxiv updated 2021/01/25 · openalex updated_date 2026/08/05
We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, obtained by a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain. Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale. Specifically, extensions are presented about parameterizing the intermediate temporal scale levels, analysing the resulting temporal dynamics and transferring the theory to a discrete implementation in terms of recursive filters over time.