Morphological Image Analysis for Computer Vision Applications

Vizilter, Yu. V.; Pyt’ev, Y.P.; Chulichkov, A. I.; Mestetskiy, Leonid M.

doi:10.1007/978-3-319-10653-3_2

Cited by 25 publications

(14 citation statements)

References 52 publications

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“…Results of quantitative experiments demonstrates that comparative filtering based on guided contrasting with considered variations of thresholded LSC and SO parameters is generally better, than approaches (Vizilter et al, 2015), (Bourdis et al,2011), excepting filter with MSMCC as LSC. As noted above (sect.4.1) we observe low robustness because there is the segmentation procedure for forming the mosaic shapes (4) of input images in the pipeline.…”

Section: Quantitative Change Detection Experimentsmentioning

confidence: 97%

“…These theories of shape have a common algebraic basis (lattice theory), but different tasks and tools. The overview of MIA and its relation to MM is given in (Vizilter et al, 2015). Morphological change detection approach is based on the analysis of morphological difference map formed as a difference between test image and its morphological projection to the shape of sample image.…”

Section: Related Workmentioning

confidence: 99%

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Change Detection via Selective Guided Contrasting Filters

Vizilter

Rubis

Zheltov

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the nonsimilar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.

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Section: Quantitative Change Detection Experimentsmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Change Detection via Selective Guided Contrasting Filters

Vizilter

Rubis

Zheltov

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

Section: Related Workmentioning

confidence: 99%

“…The overview of modern MIA results is given in (Vizilter et al, 2015). The original morphological change detection approach is based on the analysis of difference between the test image and its projection to the shape of reference image.…”

Section: Introductionmentioning

confidence: 99%

Change Detection via Morphological Comparative Filters

Vizilter¹,

Rubis²,

Zheltov³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper we propose the new change detection technique based on morphological comparative filtering. This technique generalizes the morphological image analysis scheme proposed by Pytiev. A new class of comparative filters based on guided contrasting is developed. Comparative filtering based on diffusion morphology is implemented too. The change detection pipeline contains: comparative filtering on image pyramid, calculation of morphological difference map, binarization, extraction of change proposals and testing change proposals using local morphological correlation coefficient. Experimental results demonstrate the applicability of proposed approach.

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“…The representation of images by random fields (RF) is generally accepted. To date, there is an extensive literature on image models (or RF models), for example, [1][2][3][4][5][6]. Much attention is paid to the modeling and processing of medical images for example, [7][8][9][10][11].…”

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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Morphological Image Analysis for Computer Vision Applications

Cited by 25 publications

References 52 publications

Change Detection via Selective Guided Contrasting Filters

Change Detection via Selective Guided Contrasting Filters

Change Detection via Morphological Comparative Filters

Double stochastic wave models of multidimensional random fields

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