Search results for "Convolution"
showing 10 items of 334 documents
Combining fuzzy C-mean and normalized convolution for cloud detection in IR images
2009
An important task for the cloud monitoring in several frameworks is providing maps of the cloud coverage. In this paper we present a method to detect cloudy pixels for images taken from ground by an infra-red camera. The method is a three-steps algorithm mainly based on a Fuzzy C-Mean clustering, that works on a feature space derived from the original image and the output of the reconstructed image obtained via normalized convolution. Experiments, run on several infra-red images acquired under different conditions, show that the cloud maps returned are satisfactory. © 2009 Springer Berlin Heidelberg.
Radon transform as a set of probability distributions
2009
It is proved that the Radon transform of the Wigner function gives the probability distributions related to measuring the observable operators obtained as linear combinations of position and momentum of the relevant particle. The generalization to an arbitrary number of degrees of freedom is given.
Lung CT Image Registration through Landmark-constrained Learning with Convolutional Neural Network
2020
Accurate registration of lung computed tomography (CT) image is a significant task in thorax image analysis. Recently deep learning-based medical image registration methods develop fast and achieve promising performance on accuracy and speed. However, most of them learned the deformation field through intensity similarity but ignored the importance of aligning anatomical landmarks (e.g., the branch points of airway and vessels). Accurate alignment of anatomical landmarks is essential for obtaining anatomically correct registration. In this work, we propose landmark constrained learning with a convolutional neural network (CNN) for lung CT registration. Experimental results of 40 lung 3D CT …
A characterization of the distribution of a weighted sum of gamma variables through multiple hypergeometric functions
2008
Applying the theory on multiple hypergeometric functions, the distribution of a weighted convolution of Gamma variables is characterized through explicit forms for the probability density function, the distribution function and the moments about the origin. The main results unify some previous contributions in the literature on nite convolution of Gamma distributions. We deal with computational aspects that arise from the representations in terms of multiple hypergeometric functions, introducing a new integral representation for the fourth Lauricella function F (n) D and its con uent form (n) 2 , suitable for numerical integration; some graphics of the probability density function and distr…
Explainable Reinforcement Learning with the Tsetlin Machine
2021
The Tsetlin Machine is a recent supervised machine learning algorithm that has obtained competitive results in several benchmarks, both in terms of accuracy and resource usage. It has been used for convolution, classification, and regression, producing interpretable rules. In this paper, we introduce the first framework for reinforcement learning based on the Tsetlin Machine. We combined the value iteration algorithm with the regression Tsetlin Machine, as the value function approximator, to investigate the feasibility of training the Tsetlin Machine through bootstrapping. Moreover, we document robustness and accuracy of learning on several instances of the grid-world problem.
Identification of the most informative wavelengths for non-invasive melanoma diagnostics in spectral region from 450 to 950 nm
2020
In this study 300 skin lesion (including 32 skin melanomas) multispectral data cubes were analyzed. The multi-step and single step machine learning approaches were analyzed to find the wavebands that provide the most information that helps discriminate skin melanoma from other benign pigmented lesions. The multi-step machine learning approach assumed training several models but proved itself to be ineffective. The reason for that is a necessity to train a segmentation model on a very small dataset and utilization of standard machine learning classifier which have shown poor classification performance. The single-step approach is based on a deep learning neural network. We have conducted 260…
Numerical solution of a spatio-temporal gender-structured model for hantavirus infection in rodents.
2017
In this article we describe the transmission dynamics of hantavirus in rodents using a spatio-temporal susceptible-exposed-infective-recovered (SEIR) compartmental model that distinguishes between male and female subpopulations [L.J.S. Allen, R.K. McCormack and C.B. Jonsson, Bull. Math. Biol. 68 (2006), 511--524]. Both subpopulations are assumed to differ in their movement with respect to local variations in the densities of their own and the opposite gender group. Three alternative models for the movement of the male individuals are examined. In some cases the movement is not only directed by the gradient of a density (as in the standard diffusive case), but also by a non-local convolution…
Influence of increasing convolution kernel filtering on plaque imaging with multislice CT using an ex-vivo model of coronary angiography
2005
PURPOSE: To assess the variability in attenuation of coronary plaques with multislice CT-angiography (MSCT-CA) in an ex-vivo model with varying convolution kernels. MATERIALS AND METHODS: MSCT-CA (Sensation 16, Siemens) was performed in three ex-vivo left coronary arteries after instillation of contrast material solution (Iomeprol 400 mgI/ml, dilution: 1/80). The specimens were placed in oil to simulate epicardial fat. Scan parameters: slices 16/0.75 mm, rotation time 375 ms, feed/rotation 3.0 mm, mAs 500, slice thickness 1 mm, and FOV 50 mm. Datasets were reconstructed using 4 different kernels (B30f-smooth, B36f-medium smooth, B46f-medium, and B60f-sharp). Each scan was scored for the pre…
Comparison of segmental-dependent permeability in human and in situ perfusion model in rat.
2017
Abstract Nowadays, alternative methods have been developed to predict intestinal permeability values in human as in vitro, in situ or ex vivo methods. They were developed by the necessity to avoid the problems of the human permeability experiments. However, determination of human permeability is needed to properly validate the alternative methods. For this reason, recently, Dahlgren et al. published an indirect method based on a deconvolution technique to estimate the human permeability in different gastrointestinal segments (jejunum, ileum and colon). Therefore, the objective of this research was to demonstrate that Doluisio technique is a useful method to predict the human permeability in…
Influence of convolution filtering on coronary plaque attenuation values: observations in an ex vivo model of multislice computed tomography coronary…
2007
Attenuation variability ( measured in Hounsfield Units, HU) of human coronary plaques using multislice computed tomography (MSCT) was evaluated in an ex vivo model with increasing convolution kernels. MSCT was performed in seven ex vivo left coronary arteries sunk into oil followingthe instillation of saline (1/infinity) and a 1/50 solution of contrast material ( 400 mgI/ml iomeprol). Scan parameters were: slices/ collimation, 16/0.75 mm; rotation time, 375 ms. Four convolution kernels were used: b30f-smooth, b36f-medium smooth, b46f-medium and b60f-sharp. An experienced radiologist scored for the presence of plaques and measured the attenuation in lumen, calcified and noncalcified plaques …