2,705 research outputs found
Hierarchical Image Representation Simplification Driven by Region Complexity
International audienceThis article presents a technique that arranges the elements of hierarchical representations of images according to a coarseness attribute. The choice of the attribute can be made according to prior knowledge about the content of the images and the intended application. The transformation is similar to filtering a hierarchy with a non-increasing attribute, and comprises the results of multiple simple filterings with an increasing attribute. The transformed hierarchy can be used for search space reduction prior to the image analysis process because it allows for direct access to the hierarchy elements at the same scale or a narrow range of scales
A higher-order active contour model of a `gas of circles' and its application to tree crown extraction
Many image processing problems involve identifying the region in the image
domain occupied by a given entity in the scene. Automatic solution of these
problems requires models that incorporate significant prior knowledge about the
shape of the region. Many methods for including such knowledge run into
difficulties when the topology of the region is unknown a priori, for example
when the entity is composed of an unknown number of similar objects.
Higher-order active contours (HOACs) represent one method for the modelling of
non-trivial prior knowledge about shape without necessarily constraining region
topology, via the inclusion of non-local interactions between region boundary
points in the energy defining the model. The case of an unknown number of
circular objects arises in a number of domains, e.g. medical, biological,
nanotechnological, and remote sensing imagery. Regions composed of an a priori
unknown number of circles may be referred to as a `gas of circles'. In this
report, we present a HOAC model of a `gas of circles'. In order to guarantee
stable circles, we conduct a stability analysis via a functional Taylor
expansion of the HOAC energy around a circular shape. This analysis fixes one
of the model parameters in terms of the others and constrains the rest. In
conjunction with a suitable likelihood energy, we apply the model to the
extraction of tree crowns from aerial imagery, and show that the new model
outperforms other techniques
Border collision bifurcations of stroboscopic maps in periodically driven spiking models
In this work we consider a general non-autonomous hybrid system based on the
integrate-and-fire model, widely used as simplified version of neuronal models
and other types of excitable systems. Our unique assumption is that the system
is monotonic, possesses an attracting subthreshold equilibrium point and is
forced by means of periodic pulsatile (square wave) function.\\ In contrast to
classical methods, in our approach we use the stroboscopic map (time- return
map) instead of the so-called firing-map. It becomes a discontinuous map
potentially defined in an infinite number of partitions. By applying theory for
piecewise-smooth systems, we avoid relying on particular computations and we
develop a novel approach that can be easily extended to systems with other
topologies (expansive dynamics) and higher dimensions.\\ More precisely, we
rigorously study the bifurcation structure in the two-dimensional parameter
space formed by the amplitude and the duty cycle of the pulse. We show that it
is covered by regions of existence of periodic orbits given by period adding
structures. They do not only completely describe all the possible spiking
asymptotic dynamics but also the behavior of the firing rate, which is a
devil's staircase as a function of the parameters
Volume rendering with multidimensional peak finding
Journal ArticlePeak finding provides more accurate classification for direct volume rendering by sampling directly at local maxima in a transfer function, allowing for better reproduction of high-frequency features. However, the 1D peak finding technique does not extend to higherdimensional classification. In this work, we develop a new method for peak finding with multidimensional transfer functions, which looks for peaks along the image of the ray. We use piecewise approximations to dynamically sample in transfer function space between world-space samples. As with unidimensional peak finding, this approach is useful for specifying transfer functions with greater precision, and for accurately rendering noisy volume data at lower sampling rates. Multidimensional peak finding produces comparable image quality with order-of-magnitude better performance, and can reproduce features omitted entirely by standard classification. With no precomputation or storage requirements, it is an attractive alternative to preintegration for multidimensional transfer functions
The Cluster Distribution as a Test of Dark Matter Models. IV: Topology and Geometry
We study the geometry and topology of the large-scale structure traced by
galaxy clusters in numerical simulations of a box of side 320 Mpc, and
compare them with available data on real clusters. The simulations we use are
generated by the Zel'dovich approximation, using the same methods as we have
used in the first three papers in this series. We consider the following models
to see if there are measurable differences in the topology and geometry of the
superclustering they produce: (i) the standard CDM model (SCDM); (ii) a CDM
model with (OCDM); (iii) a CDM model with a `tilted' power
spectrum having (TCDM); (iv) a CDM model with a very low Hubble
constant, (LOWH); (v) a model with mixed CDM and HDM (CHDM); (vi) a
flat low-density CDM model with and a non-zero cosmological
term (CDM). We analyse these models using a variety of
statistical tests based on the analysis of: (i) the Euler-Poincar\'{e}
characteristic; (ii) percolation properties; (iii) the Minimal Spanning Tree
construction. Taking all these tests together we find that the best fitting
model is CDM and, indeed, the others do not appear to be consistent
with the data. Our results demonstrate that despite their biased and extremely
sparse sampling of the cosmological density field, it is possible to use
clusters to probe subtle statistical diagnostics of models which go far beyond
the low-order correlation functions usually applied to study superclustering.Comment: 17 pages, 7 postscript figures, uses mn.sty, MNRAS in pres
A package for 3-D unstructured grid generation, finite-element flow solution and flow field visualization
A set of computer programs for 3-D unstructured grid generation, fluid flow calculations, and flow field visualization was developed. The grid generation program, called VGRID3D, generates grids over complex configurations using the advancing front method. In this method, the point and element generation is accomplished simultaneously, VPLOT3D is an interactive, menudriven pre- and post-processor graphics program for interpolation and display of unstructured grid data. The flow solver, VFLOW3D, is an Euler equation solver based on an explicit, two-step, Taylor-Galerkin algorithm which uses the Flux Corrected Transport (FCT) concept for a wriggle-free solution. Using these programs, increasingly complex 3-D configurations of interest to aerospace community were gridded including a complete Space Transportation System comprised of the space-shuttle orbitor, the solid-rocket boosters, and the external tank. Flow solutions were obtained on various configurations in subsonic, transonic, and supersonic flow regimes
Local Binary Patterns in Focal-Plane Processing. Analysis and Applications
Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data.
The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors.
In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presentedSiirretty Doriast
Locating Amazonian Dark Earths (ADE) in the Brazilian Amazon using Satellite Imagery
Amazonian Dark Earths (ADE) are patches of archaeological soils scattered throughout the Amazon Basin. These soils are anthropogenic and most evidence suggests that they are the result of unintentional cultural deposits as well as intentional efforts of Amerindian populations to improve the quality of their farmlands. ADE are a mixture of charcoal, organic matter and the underlying Oxisol soil. ADE are extremely fertile soils in comparison to the surrounding Oxisols and they are sought after by local residents for agricultural purposes. In the first chapter I discuss the value and physical properties of ADE in detail. Research is being conducted to learn how ADE were created and to explore the possibility of replicating them to sequester carbon and to reclaim depleted soils in the Amazon Basin. This dissertation seeks to assist in that effort by attempting to map currently unknown ADE sites hidden beneath the dense tropical forest canopy
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