scholarly journals Gaussian Process Shape Models for Bayesian Segmentation of Plant Leaves

2015 ◽  
Author(s):  
Kyle Simek ◽  
Kobus Barnard
Keyword(s):  
Gaussian Process ◽  
Plant Leaves ◽  
Shape Models ◽  
Bayesian Segmentation

Statistical shape models of plant leaves

2013 ◽  
Author(s):  
Hamid Laga ◽  
Sebastian Kurtek ◽  
Anuj Srivastava ◽  
Stanley J. Miklavcic
Keyword(s):  
Plant Leaves ◽  
Statistical Shape Models ◽  
Shape Models ◽  
Statistical Shape

Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
White Noise ◽  
Gaussian Process ◽  
Discrete Data ◽  
Hölder Exponent ◽  
Scaling Properties ◽  
Multifractional Brownian Motion ◽  
Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

Ectodesmata as Revealed By Freeze-Etch Preparations of Plant Epidermal Cell Walls

1973 ◽  
Vol 31 ◽  
pp. 468-469
Author(s):  
N.C. Lyon ◽  
W. C. Mueller
Keyword(s):  
Electron Microscopy ◽  
Acetic Acid ◽  
Nitric Acid ◽  
Oxalic Acid ◽  
Light Microscopy ◽  
Epidermal Cell ◽  
Cell Walls ◽  
Plant Viruses ◽  
Plant Leaves ◽  
Thin Sections

Schumacher and Halbsguth first demonstrated ectodesmata as pores or channels in the epidermal cell walls in haustoria of Cuscuta odorata L. by light microscopy in tissues fixed in a sublimate fixative (30% ethyl alcohol, 30 ml:glacial acetic acid, 10 ml: 65% nitric acid, 1 ml: 40% formaldehyde, 5 ml: oxalic acid, 2 g: mecuric chloride to saturation 2-3 g). Other workers have published electron micrographs of structures transversing the outer epidermal cell in thin sections of plant leaves that have been interpreted as ectodesmata. Such structures are evident following treatment with Hg++ or Ag+ salts and are only rarely observed by electron microscopy. If ectodesmata exist without such treatment, and are not artefacts, they would afford natural pathways of entry for applied foliar solutions and plant viruses.

A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

1987 ◽  
Vol 26 (03) ◽  
pp. 117-123
Author(s):  
P. Tautu ◽  
G. Wagner
Keyword(s):  
Stochastic Model ◽  
Gaussian Process ◽  
Covariance Function ◽  
Neoplastic Disease ◽  
Disease Phenotype ◽  
Probabilistic Representation ◽  
Temporal Behaviour ◽  
Continuous Trait ◽  
Continuous Parameter ◽  
Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

Identification of Continuous-time Nonlinear Systems via Local Gaussian Process Models

2014 ◽  
Vol 134 (11) ◽  
pp. 1708-1715
Author(s):  
Tomohiro Hachino ◽  
Kazuhiro Matsushita ◽  
Hitoshi Takata ◽  
Seiji Fukushima ◽  
Yasutaka Igarashi
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
Continuous Time ◽  
Process Models ◽  
Gaussian Process Models

INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL

2020 ◽  
Vol 10 (4) ◽  
pp. 333-349
Author(s):  
Kevin de Vries ◽  
Anna Nikishova ◽  
Benjamin Czaja ◽  
Gábor Závodszky ◽  
Alfons G. Hoekstra
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Cell Model ◽  
A Cell
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