The two‐dimensional and three‐dimensional processing of images provided by conventional microscopes

Martišek, Dalibor

doi:10.1002/sca.4950240602

Cited by 19 publications

(25 citation statements)

References 2 publications

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“…The first attempt was published in [11]. Statistical tools such as the variance and range of pixel values were used for the analysis of image sharpness in the original method.…”

Section: New Methods Of 2d Processing and 3d Reconstructionmentioning

confidence: 99%

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Multifocal image processing

Martišek¹,

Druckmüllerová²

2014

Math. Appl.

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Abstract. In this paper, we present a processing method for digital images from an optical microscope. High-pass type filters are generally used for image focusing. They enhance the high spatial frequencies. These filters are not appropriate if the lack of sharpness is caused by other factors. On the other hand, the (un)sharpness can be taken as an advantage and can be used for studies of the spatial distribution of structures in the observed scene. In many cases, it is possible to construct a threedimensional model of the observed object by analyzing image sharpness. Interesting two-dimensional images and a three-dimensional model can be obtained by applying the theory for multifocal image processing described in this paper. We improve the quality of the results compared to the previous methods using the Fourier transform for the analysis of local sharpness in the images.

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“…The first attempt was published in [11]. Statistical tools such as the variance and range of pixel values were used for the analysis of image sharpness in the original method.…”

Section: New Methods Of 2d Processing and 3d Reconstructionmentioning

confidence: 99%

“…We will present new results, mostly the methods based on the Fourier transform. Sections 3.1 and 3.2 are based on [11], Sections 3.3 and 3.4 are completely new. …”

Section: New Methods Of 2d Processing and 3d Reconstructionmentioning

confidence: 99%

Multifocal image processing

Martišek¹,

Druckmüllerová²

2014

Math. Appl.

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“…Three-dimensional matrix { } is stated. Its element determines statistical range, variance, or standard Fourier transform of certain neighborhood of the pixel [ ; ] in th image in the series of images [29].…”

Section: The Firstmentioning

confidence: 99%

3D Reconstruction of the Surface Using a Standard Camera

Martišek

2017

Mathematical Problems in Engineering

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This paper deals with 3D reconstructions of series of partially focussed images. Some of these methods are known in case of images which were acquired in small field of view (by confocal microscope or CCD camera, e.g.). In this case, recorded images do not differ in any geometrical transformation from each other. In case of larger samples (oversized for microscope or CCD camera), it is necessary to use wider viewing field (standard cameras, e.g.), and taken images primarily differ in scaling but may also differ in shifting and rotation too. These images cannot be used for reconstruction directly; they must be registered; that is, we must determine all transformations which the images differ and eliminate their effects. There are several ways to do this. This paper deals with the registration based on phase correlation. After this registration, it is necessary to identify the sharp parts and to compose a 2D and 3D model. Present methods are very sensitive to noise and their results are not satisfactory in many cases. We introduce a new method for 3D reconstruction which is significantly better.

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“…All processing and visualization of these data have been made by original author's software. For more information of these reconstructions and visualizations see [33] [34] [35] [36]. In this section, limestone surfaces in Figure 12 have been used for testing.…”

Section: Estimation Of the Jrc Index Of Real Samplesmentioning

confidence: 99%

Joint Rock Coefficient Estimation Based on Hausdorff Dimension

Martišek¹

2017

APM

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The strength of rock structures strongly depends inter alia on surface irregularities of rock joints. These irregularities are characterized by a coefficient of joint roughness. For its estimation, visual comparison is often used. This is rather a subjective method, therefore, fully computerized image recognition procedures were proposed. However, many of them contain imperfections, some of them even mathematical nonsenses and their application can be very dangerous in technical practice. In this paper, we recommend mathematically correct method of fully automatic estimation of the joint roughness coefficient. This method requires only the Barton profiles as a standard.

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The two‐dimensional and three‐dimensional processing of images provided by conventional microscopes

Cited by 19 publications

References 2 publications

Multifocal image processing

Multifocal image processing

3D Reconstruction of the Surface Using a Standard Camera

Joint Rock Coefficient Estimation Based on Hausdorff Dimension

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