Globally optimal regions and boundaries

We propose a new form of energy functional for the segmentation of regions in images, and an efficient method for finding its global optima. The energy can have contributions from both the region and its boundary, thus combining the best features of region- and boundary-based approaches to segmentation. By transforming the region energy into a boundary energy, we can treat both contributions on an equal footing, and solve the global optimization problem as a minimum mean weight cycle problem on a directed graph. The simple, polynomial-time algorithm requires no initialization and is highly parallelizabl

Modality-Constrained Density Estimation via Deformable Templates

Estimation of a probability density function (pdf) from its samples, while satisfying certain shape constraints, is an important problem that lacks coverage in the literature. This article introduces a novel geometric, deformable template constrained density estimator (dtcode) for estimating pdfs constrained to have a given number of modes. Our approach explores the space of thus-constrained pdfs using the set of shape-preserving transformations: an arbitrary template from the given shape class is transformed via a shape-preserving transformation to obtain the final optimal estimate. The search for this optimal transformation, under the maximum-likelihood criterion, is performed by mapping transformations to the tangent space of a Hilbert sphere, where they are effectively linearized, and can be expressed using an orthogonal basis. This framework is first applied to (univariate) unconditional densities and then extended to conditional densities. We provide asymptotic convergence rates for dtcode, and an application of the framework to the speed distributions for different traffic flows on Californian highways

Indexing of mid-resolution satellite images with structural attributes.

Satellite image classification has been a major research field for many years with its varied applications in the field of Geography, Geology, Archaeology, Environmental Sciences and Military purposes. Many different techniques have been proposed to classify satellite images with color, shape and texture features. Complex indices like Vegetation index (NDVI), Brightness index (BI) or Urban index (ISU) are used for multi-spectral or hyper-spectral satellite images. In this paper we will show the efficiency of structural features describing man-made objects in mid-resolution satellite images to describe image content. We will then show the state-of-the-art to classify large satellite images with structural features computed from road networks and urban regions extracted on small image patches cut in the large image. Fisher Linear Discriminant (FLD) analysis is used for feature selection and a one-vsrest probabilistic Gaussian kernel Support Vector Machines (SVM) classification method is used to classify the images. The classification probabilities associated with each subimage of the large image provide an estimate of the geographical class coverage

Coupled Oscillators with Chemotaxis

A simple coupled oscillator system with chemotaxis is introduced to study morphogenesis of cellular slime molds. The model successfuly explains the migration of pseudoplasmodium which has been experimentally predicted to be lead by cells with higher intrinsic frequencies. Results obtained predict that its velocity attains its maximum value in the interface region between total locking and partial locking and also suggest possible roles played by partial synchrony during multicellular development.Comment: 4 pages, 5 figures, latex using jpsj.sty and epsf.sty, to appear in J. Phys. Soc. Jpn. 67 (1998

Indexing of mid-resolution satellite images with structural attributes

Satellite image classification has been a major research field for many years with its varied applications in the field of Geography, Geology, Archaeology, Environmental Sciences and Military purposes. Many different techniques have been proposed to classify satellite images with color, shape and texture features. Complex indices like Vegetation index (NDVI), Brightness index (BI) or Urban index (ISU) are used for multi-spectral or hyper-spectral satellite images. In this paper we will show the efficiency of structural features describing man-made objects in mid-resolution satellite images to describe image content. We will then show the state-of-the-art to classify large satellite images with structural features computed from road networks and urban regions extracted on small image patches cut in the large image. Fisher Linear Discriminant (FLD) analysis is used for feature selection and a one-vsrest probabilistic Gaussian kernel Support Vector Machines (SVM) classification method is used to classify the images. The classification probabilities associated with each subimage of the large image provide an estimate of the geographical class coverage

Stellar Convective Penetration: Parameterized Theory and Dynamical Simulations

Most stars host convection zones in which heat is transported directly by fluid motion, but the behavior of convective boundaries is not well-understood. Here, we present 3D numerical simulations that exhibit penetration zones: regions where the entire luminosity could be carried by radiation, but where the temperature gradient is approximately adiabatic and convection is present. To parameterize this effect, we define the "penetration parameter" , which compares how far the radiative gradient deviates from the adiabatic gradient on either side of the Schwarzschild convective boundary. Following Roxburgh and Zahn, we construct an energy-based theoretical model in which controls the extent of penetration. We test this theory using 3D numerical simulations that employ a simplified Boussinesq model of stellar convection. The convection is driven by internal heating, and we use a height-dependent radiative conductivity. This allows us to separately specify and the stiffness of the radiative&ndash;convective boundary. We find significant convective penetration in all simulations. Our simple theory describes the simulations well. Penetration zones can take thousands of overturn times to develop, so long simulations or accelerated evolutionary techniques are required. In stars, we expect , and in this regime, our results suggest that convection zones may extend beyond the Schwarzschild boundary by up to &sim;20%&ndash;30% of a mixing length. We present a MESA stellar model of the Sun that employs our parameterization of convective penetration as a proof of concept. Finally, we discuss prospects for extending these results to more realistic stellar contexts. &nbsp;</p

The photometric variability of massive stars due to gravity waves excited by core convection

Massive stars die in catastrophic explosions, which seed the interstellar medium with heavy elements and produce neutron stars and black holes. Predictions of the explosion's character and the remnant mass depend on models of the star's evolutionary history. Models of massive star interiors can be empirically constrained by asteroseismic observations of gravity wave oscillations. Recent photometric observations reveal a ubiquitous red noise signal on massive main sequence stars; a hypothesized source of this noise is gravity waves driven by core convection. We present the first 3D simulations of massive star convection extending from the star's center to near its surface, with realistic stellar luminosities. Using these simulations, we make the first prediction of photometric variability due to convectively-driven gravity waves at the surfaces of massive stars, and find that gravity waves produce photometric variability of a lower amplitude and lower characteristic frequency than the observed red noise. We infer that the photometric signal of gravity waves excited by core convection is below the noise limit of current observations, so the red noise must be generated by an alternative process.Comment: As accepted for publication in Nature Astronomy except for final editorial revisions. Supplemental materials available online at https://doi.org/10.5281/zenodo.7764997 . We have also sonified our results to make them more accessible, see https://github.com/evanhanders/gmode_variability_paper/blob/main/sound/gmode_sonification.pd