Pseudogradient Estimation of Digital Images Interframe Geometrical Deformations

Tashlinskii, A. G.

doi:10.5772/4975

Cited by 20 publications

(6 citation statements)

References 10 publications

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“…But this method provides low choice veracity cause goal function estimations calculates based on small-size local sample. Besides, number of search domains can be up to tens of thousands [8,9], so, to make all procedures achieve the prescribed limit of iterations, needs huge computational cost. To increase the probability it should be used more reliable criteria of choice, the same as using estimations of PGP on last iteration, for example, maximum of correlation index between required fragment template and its probable location on the image [10].…”

Section: Introductionmentioning

confidence: 99%

The reliability of pattern-match searching for the fragment on image using set of pseudo-gradient procedures

Biktimirov¹,

Tashlinskii²

2017

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

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The effectiveness of pattern-match searching for the fragment on image using set of pseudo-gradient procedures covering all initial image by their workplaces is studied. Procedures control is managed to reduce computational expenses and based on analysis of penalty function and giving priority of making next iteration to procedure that has minimum value of penalty. It is considered that required fragment belongs to the domain that has a procedure which is reached prescribed limit of iterations. If it is prior unknown if there is any required fragments on the initial image, the hypothesis of their absence should be tested. If the hypothesis is not confirmed than the scan for domains with fragments should be performed. Concerning there are fragments on the image the missing probabilities are found using penalty function and limit of iterations. Herein, the proposal is considered for single and multiple required fragments.

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Section: Introductionmentioning

confidence: 99%

The reliability of pattern-match searching for the fragment on image using set of pseudo-gradient procedures

Biktimirov¹,

Tashlinskii²

2017

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

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“…Shannon mutual information (MI) is one of the most widely used similarity measure in image registration [12,13] as it provides an extremely high accuracy when images have linear and non-linear intensity distortions, occlusions and also in case of additive noise and multimodal images. Generalized Shannon MI can be defined in terms of entropy as:…”

Section: Information Measuresmentioning

confidence: 99%

“…A variant of the solution of this problem was considered in [7,8]. In this work several correlation correlations have been selected for the study: cross-correlation (the coefficient of interframe correlation) [9], the Tanimoto coefficient [10] the Kendall's rank correlation coefficient [11], and a number of informational SMs: Tsallis [12] and Shannon mutual information, F-information measures, and the entropy of the joint probability distribution [13]. Unbiased additive Gaussian noise was used as an interfering factor in the studies.…”

Section: Problem Formulationmentioning

confidence: 99%

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Effectiveness of correlation and information measures for synthesis of recurrent algorithms for estimating spatial deformations of video sequences

Tashlinskiy¹,

Zhukova²

2017

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

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A comparative analysis of the efficiency of correlation (cross-correlation, Tanimoto coefficient and Kendall's rank correlation coefficient) and information (mutual information of Tsallis and Shannon, F-information measure and entropy of the joint probability distribution) measures of image similarity for the synthesis of recursive estimation algorithms is presented for the problem of estimating parameters of spatial deformations of a sequence of images. Unbiased additive Gaussian noise was used as an interfering factor in the experimental studies. It is shown that the potentially high convergence rate of the estimated parameters and the smaller variance of the estimation error from the investigated correlation measures are ensured by the Tonimoto coefficient, and from the I-information of the F-information among the information measures. According to these criteria, the Kendall's rank correlation coefficient and the M-measure of F-information are inferior, respectively.

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“…As an a priori information for finding the initial approximations of the parameters of the templates, the area of each selected convex hull in the image is used, which is related to the area of the desired ellipse by the obvious relation: = ab. Studies have shown that three initial approximations of ellipticity (semi-axis relations) are sufficient: с = (√3) In order to increase the working range of the evaluation, the templates and convex hulls obtained for each object under study are subjected to an approximate procedure [18] of Gaussian filtering with a filter radius of 15% of the linear dimension of the object. Examples of filtered convex shells of pearlite spots are shown in Fig.…”

Section: Estimation Of Microstructural Parametersmentioning

confidence: 99%

Method for identification of perlite-class steel microstructure parameters using metallographic images

Magdeev¹,

Tashlinskiy²

2017

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

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A method for finding the microstructural parameters of low-carbon steel using its metallographic images is proposed. It allows to determine the following parameters: the ratio of perlite to ferrite phases, the parameters of grains of crystallites and their mutual arrangement; the degree of granularity of the pearlite phases. The method is aimed at predicting the strength characteristics of steel samples and consists of several stages. The preprocessing step involves color reduction, refinement of the area of interest, noise filtering, illumination refinement and histogram equalization. The segmentation of the image is associated with the search for the size, area and convex shell of grains. The stage of finding the microstructural parameters is based on the stochastic gradient-based estimation of the parameters of the segmented objects. Examples of analysis of steel oil pipeline samples with a forecast of their strength characteristics are given.

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Pseudogradient Estimation of Digital Images Interframe Geometrical Deformations

Cited by 20 publications

References 10 publications

The reliability of pattern-match searching for the fragment on image using set of pseudo-gradient procedures

The reliability of pattern-match searching for the fragment on image using set of pseudo-gradient procedures

Effectiveness of correlation and information measures for synthesis of recurrent algorithms for estimating spatial deformations of video sequences

Method for identification of perlite-class steel microstructure parameters using metallographic images

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