The specifics of pseudogradient estimation of geometric deformations in image sequences

Tashlinskii, A. G.

doi:10.1134/s1054661808040275

Cited by 19 publications

(5 citation statements)

References 1 publication

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“…The main stages of the CAM assuming the use of the Canny approach to the edge detection of objects [11], in which the Gaussian filters and the fast Fourier transform [10] are applied for noise suppression, and the Sobel operator [12] ; Λ t -amplification matrix [6]. In the identification prob-lem, the coefficient of interframe correlation coefficient (CC) is often selected as Q [14].…”

Section: Contour Analysis Methodsmentioning

confidence: 99%

Efficiency of object identification for binary images

Magdeev

Tashlinskii

2019

Computer Optics

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In this paper, a comparative analysis of the correlation-extreme method, the method of contour analysis and the method of stochastic gradient identification in the objects identification for a binary image is carried out. The results are obtained for a situation where possible deformations of an identified object with respect to a pattern can be reduced to a similarity model, that is, the pattern and the object may differ in scale, orientation angle, shift along the base axes, and additive noise. The identification of an object is understood as the recognition of its image with an estimate of the strain parameters relative to the template.

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Section: Contour Analysis Methodsmentioning

confidence: 99%

Efficiency of object identification for binary images

Magdeev

Tashlinskii

2019

Computer Optics

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“…-gradient estimation which projections can be written using mean square difference as an objective function as follows [12]: …”

Section: Problem Statementmentioning

confidence: 99%

Moving object area identification in image sequence

Tashlinskii

Smirnov

2015

2015 International Siberian Conference on Control and Communications (SIBCON)

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The paper carries out a comparative analysis of various approaches to identify area of moving object in image sequence using pixel-by-pixel estimation of inter-frame shifts. It studies projections and polar parameters of shift vectors of reference image points corresponding to the nodes as their estimated parameters. The paper proposes two methods for estimating shift vectors' field. The first method uses stochastic gradient descent algorithm to sequentially process all nodes of the image row-by-row. It processes each row bidirectionally (from the left to the right and from the right to the left). The joint processing of the results allows compensating inertia of the recursive estimation. The second method uses the correlation between rows to increase processing efficiency. It processes rows one after the other with change in direction after each row and uses obtained values to form resulting estimate for each node. The paper studies two criteria of its formation: gradient estimation minimum and correlation coefficient maximum. It also studies computational cost of the proposed algorithms.

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“…In this problem we are only interested in shift h , therefore it is reasonable to use the correlation coefficient (CC) as an objective function. Thus the number of alignment parameters is reduced to one [13], since the linear change of signal level does not change the CC. Then stochastic gradient ascent algorithm for shift estimation can be written as [14]:…”

Section: Time Shift Estimation Of Radio Pulses From Spatially Dismentioning

confidence: 99%

An algorithm for detection of radio pulses received by spatially distributed sensors

Voronov

Smirnov

Mukhometzyanov

et al. 2015

2015 International Siberian Conference on Control and Communications (SIBCON)

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A recursive algorithm for detection of radio pulses in real time based on the joint processing of unfiltered signals from spatially distributed receivers, e.g. elements of an antenna array, is proposed. The algorithm is based on a stochastic gradient ascent algorithm estimating the parameters of mutual mismatches between signals received by spatially distributed sensors. Stabilization of the estimates of signals' time alignment is chosen as one of the detection criteria. Another criterion is based on the analysis of the correlation coefficient between the aligned signals. Signal alignment is performed recurrently in real time. It is shown that if correlation coefficient is chosen as the objective functionshowing the alignment quality we need to estimate only time shift between the received signals. Experiments show the efficiency and the high probability of correct detection of radio pulses location of the proposed algorithms. To increase detection reliability the algorithms can easily be combined.

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The specifics of pseudogradient estimation of geometric deformations in image sequences

Cited by 19 publications

References 1 publication

Efficiency of object identification for binary images

Efficiency of object identification for binary images

Moving object area identification in image sequence

An algorithm for detection of radio pulses received by spatially distributed sensors

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