Moving object area identification in image sequence

Tashlinskii, A. G.; Smirnov, Pavel

doi:10.1109/sibcon.2015.7147239

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The approach to estimate the field H considered above processes images row by row as one-dimensional signals. Let us consider the possibility to increase processing efficiency by taking into account correlation of the adjacent rows of image [13,17]. For each node i; j ðÞ of the reference image, we will also form two i, j is considered better using some criterion, then it is taken as the current estimate.…”

Section: Use Of Inter-row Correlation Of Adjacent Rows Of the Imagementioning

confidence: 99%

Formation of Inter-Frame Deformation Field of Images Using Reverse Stochastic Gradient Estimation

Tashlinskii¹,

Smirnov²

2019

Pattern Recognition - Selected Methods and Applications

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The effectiveness of the use of stochastic gradient estimation to detect motion in a sequence of images is investigated. Pixel-by-pixel reverse estimation of the deformation field is used. The representation of the shift vector is considered both by the projections on the basic axes and by the polar parameters. Two approaches to estimate the parameters of the deformation field are proposed and analyzed. In the first approach, the stochastic gradient procedure sequentially processes all rows of an image to find estimates of shifts for all points of the reference image. The joint processing of the results allows compensating the inertia of the stochastic estimation. In the second approach, to improve the accuracy of estimation, the correlation of image rows is taken into account. As a criterion for the formation of the resulting estimate, the minimum of gradient estimation and correlation maximum were investigated. The computational complexity of the proposed algorithms is investigated. The algorithms are compared with the MVFAST algorithm. Examples of experimental results on the formation of the deformation field, the selection of a moving object area, and the finding of the movement and trajectory parameters of a moving object in a video sequence are given.

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Section: Use Of Inter-row Correlation Of Adjacent Rows Of the Imagementioning

confidence: 99%

Formation of Inter-Frame Deformation Field of Images Using Reverse Stochastic Gradient Estimation

Tashlinskii¹,

Smirnov²

2019

Pattern Recognition - Selected Methods and Applications

Self Cite

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Technique to model the movement of the scene using image sequence

Tashlinskii

Kovalenko

Smirnov³

et al. 2019

J. Phys.: Conf. Ser.

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The paper proposes a technique to model the movement of a scene based on backward stochastic gradient pixel-by-pixel estimation of the deformation field. Calculation of the moving object’s area is considered as the task of testing the hypothesis that the image grid nodes belong to the area of motion. The paper presents the estimated two kinds of errors: false positive and false negative. The obtained results are compared with the results of the MVFAST algorithm. The increase in the signal-to-noise ratio of the image of a moving object is achieved by combining the frames of the video sequence under study. To combine conjugate points, high-speed recurrent algorithms are used that do not require a priori information. The paper presents an example of estimation of object’s trajectory parameters using the technique. The interframe geometric deformations of the video sequence are used as intermediate parameters of the trajectory.

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Probabilistic finite modeling of stochastic estimation of image inter-frame geometric deformations

Tashlinskii

Safina

Kovalenko

2019

J. Phys.: Conf. Ser.

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The paper proposes an approach to probabilistic finite modeling of the process of stochastic estimation of the parameters of interframe geometric deformations of images. As the quantities characterizing the state of the stochastic procedure at each iteration of the estimation, the demolition probabilities of the formed parameter estimates (improvements, deterioration, or no change in the vector of estimates in the parameter space) are used. At the same time, the models of images and noises are given by probability distribution densities and autocorrelation functions. This made it possible to simplify the description of a complex set of factors influencing the formation of estimates. The use of adaptive limitation of the parameter space used for modeling reduces the computational cost several times while maintaining the adequacy of the model. In practice, probabilistic modeling can be used to find the accuracy and probability characteristics of stochastic algorithms for estimating interframe deformations of images for a given number of iterations.

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Moving object area identification in image sequence

Cited by 3 publications

References 11 publications

Formation of Inter-Frame Deformation Field of Images Using Reverse Stochastic Gradient Estimation

Formation of Inter-Frame Deformation Field of Images Using Reverse Stochastic Gradient Estimation

Technique to model the movement of the scene using image sequence

Probabilistic finite modeling of stochastic estimation of image inter-frame geometric deformations

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