Search results for "62P35"

showing 2 items of 2 documents

A Bayesian Multilevel Random-Effects Model for Estimating Noise in Image Sensors

2020

Sensor noise sources cause differences in the signal recorded across pixels in a single image and across multiple images. This paper presents a Bayesian approach to decomposing and characterizing the sensor noise sources involved in imaging with digital cameras. A Bayesian probabilistic model based on the (theoretical) model for noise sources in image sensing is fitted to a set of a time-series of images with different reflectance and wavelengths under controlled lighting conditions. The image sensing model is a complex model, with several interacting components dependent on reflectance and wavelength. The properties of the Bayesian approach of defining conditional dependencies among parame…

FOS: Computer and information sciencesMean squared errorC.4Computer scienceBayesian probabilityG.3ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONInference02 engineering and technologyBayesian inferenceStatistics - Applications0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineeringApplications (stat.AP)Electrical and Electronic EngineeringImage sensorI.4.1C.4; G.3; I.4.1Pixelbusiness.industryImage and Video Processing (eess.IV)020206 networking & telecommunicationsPattern recognitionStatistical modelElectrical Engineering and Systems Science - Image and Video ProcessingRandom effects modelNoise62P30 62P35 62F15 62J05Signal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftware
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Synchronization and fluctuations for interacting stochastic systems with individual and collective reinforcement

2020

The Pólya urn is the paradigmatic example of a reinforced stochastic process. It leads to a random (non degenerated) time-limit. The Friedman urn is a natural generalization whose a.s. time-limit is not random anymore. In this work, in the stream of previous recent works, we introduce a new family of (finite) systems of reinforced stochastic processes, interacting through an additional collective reinforcement of mean field type. The two reinforcement rules strengths (one componentwise, one collective) are tuned through (possibly) different rates n −γ. In the case the reinforcement rates are like n −1 , these reinforcements are of Pólya or Friedman type as in urn contexts and may thus lead …

[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Interacting random systemssynchronisation[MATH] Mathematics [math]Almost sure convergenceReinforced stochastic processes[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]62P35Secondary 62L2060F05Central limit theoremsFluctuationsFluctuations MSC2010 Classification Primary 60K3560F15[MATH]Mathematics [math]stable convergence
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