117 research outputs found
ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π₯Π°ΡΠ° Π΄Π»Ρ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ Π΄ΠΈΡΠΏΠ΅ΡΡΠ½ΠΎΡΡΠΈ Π½Π°ΠΊΠ»Π°Π΄ΡΠ²Π°ΡΡΠΈΡ ΡΡ ΡΠ°ΡΡΠΈΡ ΠΈ ΠΈΡ Π°Π³Π»ΠΎΠΌΠ΅ΡΠ°ΡΠΎΠ²
The dispersion control of micro- and nanoparticles by their images is of great importance for ensuring the specified properties of the particles themselves and materials based on them. The aim of this article was to consider the possibilities of using the Hough transform for dispersion control of overlapping particles and their agglomerates. Analysis of the application of the Hough transform for overlapping particles and their agglomerates showed the following. The particularities of the conventional implementation lead to the preferred registration of large particles, the shift of the centers of overlapping particles, and the distortion of the size values. To use the Hough transform correctly, fine-tuning of all its parameters is required. To automate this process, the dependences of the number and size of particles recorded in the image on the parameters of the Hough transform was investigated. The studies were carried out on test images with a known number and size of particles. The results showed that when the threshold parameters of the Hough transform change, the number of detected particles stabilizes near their optimal values. When the size range of particles detected by the Hough transform changes, the histogram of the particle size distribution changes. In this case, the optimal width of the range is determined by the most stable extremes of the histogram. The maximum center-to-center distance is set at least half of the optimal range. The configuration algorithm is described and implemented. It implies repeatedly running the Hough transform with different combinations of parameters. The algorithm includes stages of coarse and fine-tuning, which allows to getting closer to the optimal parameters. The efficiency of the algorithm has been confirmed on test and real images. Tests have shown that the errors in determining the size and number of particles of the multi-pass Hough transform are on the same level or exceed these indicators for analog methods.ΠΠΎΠ½ΡΡΠΎΠ»Ρ Π΄ΠΈΡΠΏΠ΅ΡΡΠ½ΠΎΡΡΠΈ ΠΌΠΈΠΊΡΠΎ- ΠΈ Π½Π°Π½ΠΎΡΠ°ΡΡΠΈΡ ΠΏΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ ΠΈΠΌΠ΅Π΅Ρ Π±ΠΎΠ»ΡΡΠΎΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ Π΄Π»Ρ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Π½Π½ΡΡ
ΡΠ²ΠΎΠΉΡΡΠ² ΡΠ°ΠΌΠΈΡ
ΡΠ°ΡΡΠΈΡ ΠΈ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ² Π½Π° ΠΈΡ
ΠΎΡΠ½ΠΎΠ²Π΅. Π¦Π΅Π»ΡΡ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΡΠ²Π»ΡΠ»ΠΎΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π₯Π°ΡΠ° Π΄Π»Ρ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ Π΄ΠΈΡΠΏΠ΅ΡΡΠ½ΠΎΡΡΠΈ Π½Π°ΠΊΠ»Π°Π΄ΡΠ²Π°ΡΡΠΈΡ
ΡΡ ΡΠ°ΡΡΠΈΡ ΠΈ ΠΈΡ
Π°Π³Π»ΠΎΠΌΠ΅ΡΠ°ΡΠΎΠ². ΠΠ½Π°Π»ΠΈΠ· ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π₯Π°ΡΠ° Π΄Π»Ρ Π½Π°ΠΊΠ»Π°Π΄ΡΠ²Π°ΡΡΠΈΡ
ΡΡ ΡΠ°ΡΡΠΈΡ ΠΈ ΠΈΡ
Π°Π³Π»ΠΎΠΌΠ΅ΡΠ°ΡΠΎΠ² ΠΏΠΎΠΊΠ°Π·Π°Π» ΡΠ»Π΅Π΄ΡΡΡΠ΅Π΅. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΠΊΠΎΠ½Π²Π΅Π½ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡ ΠΊ ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ Π±ΠΎΠ»ΡΡΠΈΡ
ΡΠ°ΡΡΠΈΡ, ΡΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ΅Π½ΡΡΠΎΠ² ΠΏΠ΅ΡΠ΅ΠΊΡΡΠ²Π°ΡΡΠΈΡ
ΡΡ ΡΠ°ΡΡΠΈΡ, ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΡ ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ². ΠΠ»Ρ ΠΊΠΎΡΡΠ΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π₯Π°ΡΠ° ΡΡΠ΅Π±ΡΠ΅ΡΡΡ ΡΠΎΡΠ½Π°Ρ Π½Π°ΡΡΡΠΎΠΉΠΊΠ° Π²ΡΠ΅Ρ
Π΅Π³ΠΎ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ². ΠΠ»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ ΡΠ°ΠΊΠΎΠΉ Π½Π°ΡΡΡΠΎΠΉΠΊΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° ΠΈ ΡΠ°Π·ΠΌΠ΅ΡΠ° ΡΠ΅Π³ΠΈΡΡΡΠΈΡΡΠ΅ΠΌΡΡ
Π½Π° ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΈ ΡΠ°ΡΡΠΈΡ ΠΎΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π₯Π°ΡΠ°. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈΡΡ Π½Π° ΡΠ΅ΡΡΠΎΠ²ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡ
Ρ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΠΌ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎΠΌ ΠΈ ΡΠ°Π·ΠΌΠ΅ΡΠ°ΠΌΠΈ ΡΠ°ΡΡΠΈΡ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΠΏΡΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΈ ΠΏΠΎΡΠΎΠ³ΠΎΠ²ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π₯Π°ΡΠ° ΡΠΈΡΠ»ΠΎ ΡΠ΅Π³ΠΈΡΡΡΠΈΡΡΠ΅ΠΌΡΡ
ΡΠ°ΡΡΠΈΡ ΡΡΠ°Π±ΠΈΠ»ΠΈΠ·ΠΈΡΡΠ΅ΡΡΡ Π²Π±Π»ΠΈΠ·ΠΈ ΠΈΡ
ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ. ΠΡΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΈ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ², ΡΠ΅Π³ΠΈΡΡΡΠΈΡΡΠ΅ΠΌΡΡ
ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π₯Π°ΡΠ° ΡΠ°ΡΡΠΈΡ ΠΈΠ·ΠΌΠ΅Π½ΡΠ΅ΡΡΡ Π³ΠΈΡΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ°ΡΡΠΈΡ ΠΏΠΎ ΡΠ°Π·ΠΌΠ΅ΡΠ°ΠΌ. ΠΡΠΈ ΡΡΠΎΠΌ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½Π°Ρ ΡΠΈΡΠΈΠ½Π° Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΡΡΡ ΠΏΠΎ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΡΠΎΠΉΡΠΈΠ²ΡΠΌ ΡΠΊΡΡΡΠ΅ΠΌΡΠΌΠ°ΠΌ Π³ΠΈΡΡΠΎΠ³ΡΠ°ΠΌΠΌΡ. ΠΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ΅ ΠΌΠ΅ΠΆΡΠ΅Π½ΡΡΠΎΠ²ΠΎΠ΅ ΡΠ°ΡΡΡΠΎΡΠ½ΠΈΠ΅ ΡΡΡΠ°Π½Π°Π²Π»ΠΈΠ²Π°Π΅ΡΡΡ Π½Π΅ ΠΌΠ΅Π½Π΅Π΅ ΠΏΠΎΠ»ΠΎΠ²ΠΈΠ½Ρ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π°. ΠΠΏΠΈΡΠ°Π½ ΠΈ ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π½Π°ΡΡΡΠΎΠΉΠΊΠΈ, ΠΏΠΎΠ΄ΡΠ°Π·ΡΠΌΠ΅Π²Π°ΡΡΠΈΠΉ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡΠ°ΡΠ½ΡΠΉ Π·Π°ΠΏΡΡΠΊ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π₯Π°ΡΠ° Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌΠΈ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΡΠΌΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ². ΠΠ»Π³ΠΎΡΠΈΡΠΌ Π²ΠΊΠ»ΡΡΠ°Π΅Ρ ΡΡΠ°ΠΏΡ Π³ΡΡΠ±ΠΎΠΉ ΠΈ ΡΠΎΡΠ½ΠΎΠΉ Π½Π°ΡΡΡΠΎΠΉΠΊΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠ΅ ΡΠΎΡΠ½Π΅Π΅ ΠΏΡΠΈΠ±Π»ΠΈΠ·ΠΈΡΡΡ ΠΊ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΌ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌ. Π Π°Π±ΠΎΡΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π΅Π½Π° Π½Π° ΡΠ΅ΡΡΠΎΠ²ΡΡ
ΠΈ ΡΠ΅Π°Π»ΡΠ½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡ
. ΠΡΠΈ ΡΡΠΎΠΌ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎΡΡΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ² ΠΈ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° ΡΠ°ΡΡΠΈΡ ΠΌΠ½ΠΎΠ³ΠΎΠΏΡΠΎΡ
ΠΎΠ΄ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π₯Π°ΡΠ° Π½Π°Ρ
ΠΎΠ΄ΡΡΡΡ Π½Π° ΠΎΠ΄Π½ΠΎΠΌ ΡΡΠΎΠ²Π½Π΅ ΠΈΠ»ΠΈ ΠΏΡΠ΅Π²ΠΎΡΡ
ΠΎΠ΄ΡΡ Π΄Π°Π½Π½ΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ²-Π°Π½Π°Π»ΠΎΠ³ΠΎΠ²
Application of the Hough Transform to Dispersion Control of Overlapping Particles and Their Agglomerates
The dispersion control of micro- and nanoparticles by their images is of great importance for ensuring the specified properties of the particles themselves and materials based on them. The aim of this article was to consider the possibilities of using the Hough transform for dispersion control of overlapping particles and their agglomerates. Analysis of the application of the Hough transform for overlapping particles and their agglomerates showed the following. The particularities of the conventional implementation lead to the preferred registration of large particles, the shift of the centers of overlapping particles, and the distortion of the size values. To use the Hough transform correctly, fine-tuning of all its parameters is required. To automate this process, the dependences of the number and size of particles recorded in the image on the parameters of the Hough transform was investigated. The studies were carried out on test images with a known number and size of particles. The results showed that when the threshold parameters of the Hough transform change, the number of detected particles stabilizes near their optimal values. When the size range of particles detected by the Hough transform changes, the histogram of the particle size distribution changes. In this case, the optimal width of the range is determined by the most stable extremes of the histogram. The maximum center-to-center distance is set at least half of the optimal range. The configuration algorithm is described and implemented. It implies repeatedly running the Hough transform with different combinations of parameters. The algorithm includes stages of coarse and fine-tuning, which allows to getting closer to the optimal parameters. The efficiency of the algorithm has been confirmed on test and real images. Tests have shown that the errors in determining the size and number of particles of the multi-pass Hough transform are on the same level or exceed these indicators for analog methods
Review : Deep learning in electron microscopy
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy
Engineering Education and Research Using MATLAB
MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks
Foetal echocardiographic segmentation
Congenital heart disease affects just under one percentage of all live births [1].
Those defects that manifest themselves as changes to the cardiac chamber volumes
are the motivation for the research presented in this thesis.
Blood volume measurements in vivo require delineation of the cardiac chambers and
manual tracing of foetal cardiac chambers is very time consuming and operator
dependent. This thesis presents a multi region based level set snake deformable
model applied in both 2D and 3D which can automatically adapt to some extent
towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts.
The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD).
The level set methods presented in this thesis have an optional shape prior term for
constraining the segmentation by a template registered to the image in the presence
of shadowing and heavy noise.
When applied to real data in the absence of the template the MSSCD algorithm is
initialised from seed primitives placed at the centre of each cardiac chamber. The
voxel statistics inside the chamber is determined before evolution. The MSSCD stops
at open boundaries between two chambers as the two approaching level set fronts
meet. This has significance when determining volumes for all cardiac compartments
since cardiac indices assume that each chamber is treated in isolation. Comparison
of the segmentation results from the implemented snakes including a previous level
set method in the foetal cardiac literature show that in both 2D and 3D on both real
and synthetic data, the MSSCD formulation is better suited to these types of data.
All the algorithms tested in this thesis are within 2mm error to manually traced
segmentation of the foetal cardiac datasets. This corresponds to less than 10% of
the length of a foetal heart. In addition to comparison with manual tracings all the
amorphous deformable model segmentations in this thesis are validated using a
physical phantom. The volume estimation of the phantom by the MSSCD
segmentation is to within 13% of the physically determined volume
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