Doubly stochastic models of images

Васильев, Константин Константинович; Dementiev, Vitaly; Andriyanov, Nikita

doi:10.1134/s1054661815010204

Cited by 31 publications

(10 citation statements)

References 2 publications

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“…V International Conference on "Information Technology and Nanotechnology" (ITNT-2019) Image Processing and Earth Remote Sensing V R Krasheninnikov, A U Subbotin V International Conference on "Information Technology and Nanotechnology" (ITNT-2019) 42 However, this model describes only homogeneous RF. In [15,16], a double stochastic autoregressive model of RF was proposed for describing inhomogeneous images. In this model, the first wave RF (control field) sets the parameters of the second RF (controlled field), which turns out to be non-uniform, since its parameters randomly vary in space.…”

Section: Introductionmentioning

confidence: 99%

Double stochastic wave models of multidimensional random fields

Krasheninnikov

Subbotin

2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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The paper deals with the development of mathematical models of random fields to describe and simulate images. In the wave model, a random field is the result of the influence of perturbations (waves) that occur at random times in random places and have random shapes. This model allows representing and simulate isotropic and anisotropic images (and their temporal sequences) defined on arbitrary areas of multidimensional space, as well as on any surfaces. The problems of correlation analysis and synthesis can be relatively easily solved. However, this model allows representing only homogeneous fields. In this paper, we consider «double stochastic» wave models, when the first wave random field (control field) sets the parameters of the second (controlled field). As a result, the controlled field becomes nonuniform, since its parameters vary randomly. We also consider options when two fields mutually influence each other. These models allow us to represent and simulate multidimensional inhomogeneous images (and their temporal sequences), as well as systems of such images with mutual correlations.

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Section: Introductionmentioning

confidence: 99%

Double stochastic wave models of multidimensional random fields

Krasheninnikov

Subbotin

2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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“…Statistical methods [1][2][3][4][5][6][7][8][9][10] are used fruitfully for solving problems on image representation and pro cessing. Such problems are as follows: to detect anom alies, to improve the noisy images quality, to estimate deformations of multidimensional images, and many others.…”

Section: Introductionmentioning

confidence: 99%

“…In the present work, for restoring the fragment of an image on the basis of the existing information, we propose to estimate the parameters of a sufficiently general doubly stochastic model [8][9][10]. For increas ing the restoration accuracy, the pseudogradient algo rithm [6] for estimating the model's parameters and the vectorial Kalman filter [10] for determining the parameters in the vicinity of the damaged segment are used sequentially.…”

Section: Introductionmentioning

confidence: 99%

Application of mixed models for solving the problem on restoring and estimating image parameters

Васильев

Dementiev

Andriyanov

2016

Pattern Recognit. Image Anal.

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“…The conducted studies [11][12][13] show that to form such filters it is possible to use doubly stochastic image models, which allow describing inhomogeneous signals [14]. As an example, consider the following model [8]:  with a Gaussian probability distribution density can be described by the following autoregressive equations: …”

Section: Algorithms For Filtering and Detecting Anomalies Against A Bmentioning

confidence: 99%

Anomalies detection on spatially inhomogeneous polyzonal images

Andriyanov¹,

Vasiliev²,

Dementiev³

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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The text deals with the problem of detecting anomalies on a background of multi-dimensional images. We synthesized a detection algorithm based on the use of doubly stochastic models of random fields and which requires pre-filtering the image. We propose to use the modified Kalman filter. We also investigated an efficiency of extended signals detection on real images. It is shown that the resulting algorithm has a higher efficiency than the known algorithms which based on the traditional autoregressive image description. The gain is explained by more adequate description of the real inhomogeneous material using doubly stochastic models.

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Doubly stochastic models of images

Cited by 31 publications

References 2 publications

Double stochastic wave models of multidimensional random fields

Double stochastic wave models of multidimensional random fields

Application of mixed models for solving the problem on restoring and estimating image parameters

Anomalies detection on spatially inhomogeneous polyzonal images

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