50 research outputs found
Time-causal and time-recursive spatio-temporal receptive fields
We present an improved model and theory for time-causal and time-recursive
spatio-temporal receptive fields, based on 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 (i) parameterizing the
intermediate temporal scale levels, (ii) analysing the resulting temporal
dynamics, (iii) transferring the theory to a discrete implementation, (iv)
computing scale-normalized spatio-temporal derivative expressions for
spatio-temporal feature detection and (v) computational modelling of receptive
fields in the lateral geniculate nucleus (LGN) and the primary visual cortex
(V1) in biological vision.
We show that by distributing the intermediate temporal scale levels according
to a logarithmic distribution, we obtain much faster temporal response
properties (shorter temporal delays) compared to a uniform distribution.
Specifically, these kernels converge very rapidly to a limit kernel possessing
true self-similar scale-invariant properties over temporal scales, thereby
allowing for true scale invariance over variations in the temporal scale,
although the underlying temporal scale-space representation is based on a
discretized temporal scale parameter.
We show how scale-normalized temporal derivatives can be defined for these
time-causal scale-space kernels and how the composed theory can be used for
computing basic types of scale-normalized spatio-temporal derivative
expressions in a computationally efficient manner.Comment: 39 pages, 12 figures, 5 tables in Journal of Mathematical Imaging and
Vision, published online Dec 201
Branch Mode Selection during Early Lung Development
Many organs of higher organisms, such as the vascular system, lung, kidney,
pancreas, liver and glands, are heavily branched structures. The branching
process during lung development has been studied in great detail and is
remarkably stereotyped. The branched tree is generated by the sequential,
non-random use of three geometrically simple modes of branching (domain
branching, planar and orthogonal bifurcation). While many regulatory components
and local interactions have been defined an integrated understanding of the
regulatory network that controls the branching process is lacking. We have
developed a deterministic, spatio-temporal differential-equation based model of
the core signaling network that governs lung branching morphogenesis. The model
focuses on the two key signaling factors that have been identified in
experiments, fibroblast growth factor (FGF10) and sonic hedgehog (SHH) as well
as the SHH receptor patched (Ptc). We show that the reported biochemical
interactions give rise to a Schnakenberg-type Turing patterning mechanisms that
allows us to reproduce experimental observations in wildtype and mutant mice.
The kinetic parameters as well as the domain shape are based on experimental
data where available. The developed model is robust to small absolute and large
relative changes in the parameter values. At the same time there is a strong
regulatory potential in that the switching between branching modes can be
achieved by targeted changes in the parameter values. We note that the sequence
of different branching events may also be the result of different growth
speeds: fast growth triggers lateral branching while slow growth favours
bifurcations in our model. We conclude that the FGF10-SHH-Ptc1 module is
sufficient to generate pattern that correspond to the observed branching modesComment: Initially published at PLoS Comput Bio
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure