2,867 research outputs found
X-ray based tree ring analyses
In this thesis, two x-ray based dendro-analyses (batch-wise microdensitometry and energy dispersive x-ray fluorescence (EDXRF)) and the conditions under which these methods could be used on the two typical boreal conifers Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) were evaluated. For density measurements using batch scanning x-ray densitometry, sample preparation and density calibration are vital to acquire high resolution and precision in densitometric measurements. Thickness and alignment should be adapted to give optimal resolution without loss of precision. Samples should be extracted to remove resins before wood density measurements. Ca, Mn, Fe, and Sr and in most cases K and Zn could thus be measured in an efficient way using EDXRF. Significant differences in concentrations between tree rings were found for all of these elements except Sr, indicating that tree ring concentrations of these elements could be correlated to changes in the tree environment. For Mn a correlation between soil pH and Mn concentration in tree rings was found. Other correlations between tree ring element concentration and tree environmental factors were also found. For most elements in tree-rings of Scots pine and Norway spruce it seems that the concentration is due to environmental conditions during the year the tree-ring was developed and several years afterwards. It is concluded that microdensitometry and EDXRF analyses on increment cores are cost-efficient and non-destructive analyses of wood properties. Batch-wise, x-ray based dendrochemical analysis provides opportunities for more property-based use of wood raw material and for environmental monitoring
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
Invariance of visual operations at the level of receptive fields
Receptive field profiles registered by cell recordings have shown that
mammalian vision has developed receptive fields tuned to different sizes and
orientations in the image domain as well as to different image velocities in
space-time. This article presents a theoretical model by which families of
idealized receptive field profiles can be derived mathematically from a small
set of basic assumptions that correspond to structural properties of the
environment. The article also presents a theory for how basic invariance
properties to variations in scale, viewing direction and relative motion can be
obtained from the output of such receptive fields, using complementary
selection mechanisms that operate over the output of families of receptive
fields tuned to different parameters. Thereby, the theory shows how basic
invariance properties of a visual system can be obtained already at the level
of receptive fields, and we can explain the different shapes of receptive field
profiles found in biological vision from a requirement that the visual system
should be invariant to the natural types of image transformations that occur in
its environment.Comment: 40 pages, 17 figure
Idealized computational models for auditory receptive fields
This paper presents a theory by which idealized models of auditory receptive
fields can be derived in a principled axiomatic manner, from a set of
structural properties to enable invariance of receptive field responses under
natural sound transformations and ensure internal consistency between
spectro-temporal receptive fields at different temporal and spectral scales.
For defining a time-frequency transformation of a purely temporal sound
signal, it is shown that the framework allows for a new way of deriving the
Gabor and Gammatone filters as well as a novel family of generalized Gammatone
filters, with additional degrees of freedom to obtain different trade-offs
between the spectral selectivity and the temporal delay of time-causal temporal
window functions.
When applied to the definition of a second-layer of receptive fields from a
spectrogram, it is shown that the framework leads to two canonical families of
spectro-temporal receptive fields, in terms of spectro-temporal derivatives of
either spectro-temporal Gaussian kernels for non-causal time or the combination
of a time-causal generalized Gammatone filter over the temporal domain and a
Gaussian filter over the logspectral domain. For each filter family, the
spectro-temporal receptive fields can be either separable over the
time-frequency domain or be adapted to local glissando transformations that
represent variations in logarithmic frequencies over time. Within each domain
of either non-causal or time-causal time, these receptive field families are
derived by uniqueness from the assumptions.
It is demonstrated how the presented framework allows for computation of
basic auditory features for audio processing and that it leads to predictions
about auditory receptive fields with good qualitative similarity to biological
receptive fields measured in the inferior colliculus (ICC) and primary auditory
cortex (A1) of mammals.Comment: 55 pages, 22 figures, 3 table
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
Separable 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, 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.Comment: 12 pages, 2 figures, 2 tables. arXiv admin note: substantial text
overlap with arXiv:1404.203
Provably scale-covariant networks from oriented quasi quadrature measures in cascade
This article presents a continuous model for hierarchical networks based on a
combination of mathematically derived models of receptive fields and
biologically inspired computations. Based on a functional model of complex
cells in terms of an oriented quasi quadrature combination of first- and
second-order directional Gaussian derivatives, we couple such primitive
computations in cascade over combinatorial expansions over image orientations.
Scale-space properties of the computational primitives are analysed and it is
shown that the resulting representation allows for provable scale and rotation
covariance. A prototype application to texture analysis is developed and it is
demonstrated that a simplified mean-reduced representation of the resulting
QuasiQuadNet leads to promising experimental results on three texture datasets.Comment: 12 pages, 3 figures, 1 tabl
Pulse of inflammatory proteins in the pregnant uterus of European polecats (Mustela putorius) leading to the time of implantation
Uterine secretory proteins protect the uterus and conceptuses against infection, facilitate implantation, control cellular damage resulting from implantation, and supply pre-implantation embryos with nutrients. Unlike in humans, the early conceptus of the European polecat (Mustela putorius; ferret) grows and develops free in the uterus until implanting at about 12 days after mating. We found that the proteins appearing in polecat uteri changed dramatically with time leading to implantation. Several of these proteins have also been found in pregnant uteri of other eutherian mammals. However, we found a combination of two increasingly abundant proteins that have not been recorded before in pre-placentation uteri. First, the broad-spectrum proteinase inhibitor α2-macroglobulin rose to dominate the protein profile by the time of implantation. Its functions may be to limit damage caused by the release of proteinases during implantation or infection, and to control other processes around sites of implantation. Second, lipocalin-1 (also known as tear lipocalin) also increased substantially in concentration. This protein has not previously been recorded as a uterine secretion in pregnancy in any species. If polecat lipocalin-1 has similar biological properties to that of humans, then it may have a combined function in antimicrobial protection and transporting or scavenging lipids. The changes in the uterine secretory protein repertoire of European polecats is therefore unusual, and may be representative of pre-placentation supportive uterine secretions in mustelids (otters, weasels, badgers, mink, wolverines) in general
Inability of spatial transformations of CNN feature maps to support invariant recognition
A large number of deep learning architectures use spatial transformations of
CNN feature maps or filters to better deal with variability in object
appearance caused by natural image transformations. In this paper, we prove
that spatial transformations of CNN feature maps cannot align the feature maps
of a transformed image to match those of its original, for general affine
transformations, unless the extracted features are themselves invariant. Our
proof is based on elementary analysis for both the single- and multi-layer
network case. The results imply that methods based on spatial transformations
of CNN feature maps or filters cannot replace image alignment of the input and
cannot enable invariant recognition for general affine transformations,
specifically not for scaling transformations or shear transformations. For
rotations and reflections, spatially transforming feature maps or filters can
enable invariance but only for networks with learnt or hardcoded rotation- or
reflection-invariant featuresComment: 22 pages, 3 figure
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