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[pt] CARACTERIZAÇÃO DE COMPÓSITOS CIMENTÍCIOS REFORÇADOS COM FIBRAS: APRENDIZAGEM PROFUNDA, MICROTC DE RAIO X INSITU, CORRELAÇÃO DIGITAL DE VOLUME / [en] CHARACTERIZATION OF STRAIN-HARDENING CEMENT-BASED COMPOSITES: DEEP LEARNING, IN-SITU X-RAY MICROCT AND DIGITAL VOLUME CORRELATIONRENATA LORENZONI 29 December 2021 (has links)
[pt] entendimento do macro comportamento dos materiais, este trabalho apresenta
soluções inovadoras para a análise de imagens 3D obtidas por microtomografia
computadorizada de raios-X (microCT). O material estudado conhecido
pelo termo em inglês “strain-hardening cement-based composites”
ou pela abreviação SHCC é um compósito cimentício reforçado com fibras
que atinge deformações significativas através da formação de múltiplas fissuras,
estabelecendo um material cimentício com característica pseudo-dúctil.
O primeiro desafio deste trabalho foi reconhecer e quantificar as fases constituintes
nas imagens 3D de SHCC obtidas por microCT. Materiais com
estruturas complexas podem apresentar imagens em que as fases internas
não podem ser distinguidas pela técnica de limiarização clássica, exigindo
o uso de outra técnica como a segmentação por Deep Learning (DL). Portanto,
este trabalho utilizou DL como solução para esta tarefa. Desta forma,
as características de cada fases puderam ser correlacionadas ao comportamento
mecânico macro do material em ensaios de microCT in-situ. Outro
método moderno de análise de imagens 3D utilizado foi a correlação digital
de volume (em inglês, digital volume correlation - DVC). O DVC é uma
técnica que estima o campo de deformação sobre todo o volume da amostra,
correlacionando as imagens 3D nos estados descarregado e carregado.
Assim, as imagens obtidas nos ensaios de tração e compressão in-situ puderam
ter seus deslocamentos internos medidos e deformações calculadas. Em
síntese, este trabalho trouxe avanços ao campo do processamento digital e
análise de imagens 3D, aplicadas a materiais cimentícios, mas que também
podem se adaptar à análise de diversos materiais. / [en] Considering the importance of micro and mesoscale analyses to understand
the macro behavior of materials, this work brings innovative solutions
for analyzing 3D images obtained by X-ray micro-computed tomography
(microCT). The studied material was the strain-hardening cement-based
composites (SHCC), a fiber reinforced cementitious composite that achieves
significant deformations through multiple cracks formation, resulting in a
cementitious material with pseudo ductile features. The first challenge of
this work was to recognize and quantify the constituent phases in the 3D
images of SHCC obtained by microCT. Materials with complex structures
may present images in which the internal phases cannot be distinguished by
the classical thresholding technique, requiring the use of another technique
such as segmentation by Deep Learning (DL). Therefore, this work used
DL as a solution for this task. Then, the features of each phase could
be correlated to the macro mechanical behavior of the material in in-situ
microCT tests. Another modern method for analyzing 3D images used was
the digital volume correlation (DVC). DVC is a technique that estimates
full-field strain in 3D over the entire volume of the specimen by correlating
imaging volumes of the specimen in unloaded and loaded states. Thus,
the images obtained from tensile and compression in-situ tests could have
their internal displacements measured and strain calculated. In summary,
this work brought advances to the 3D image processing and analysis field,
applied to cementitious materials, but which could also adapt for the
analysis of various materials.
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Matematické metody pro zpracování obrazu v biologických pozorováních / Mathematical Methods for Image Processing in Biological ObservationsZikmund, Tomáš January 2014 (has links)
The dissertation deals with the image processing in digital holographic microscopy and X-ray computed tomography. The focus of the work lies in the proposal of data processing techniques to meet the needs of the biological experiments. Transmitted light holographic microscopy is particularly used for quantitative phase imaging of transparent microscopic objects such as living cells. The phase images are affected by the phase aberrations that make the analysis particularly difficult. Here, we present a novel algorithm for dynamical processing of living cells phase images in a time-lapse sequence. The algorithm compensates for the deformation of a phase image using weighted least squares surface fitting. Moreover, it identifies and segments the individual cells in the phase image. This property of the algorithm is important for real-time cell quantitative phase imaging and instantaneous control of the course of the experiment. The efficiency of the propounded algorithm is demonstrated on images of rat fibrosarcoma cells using an off-axis holographic microscope. High resolution X-ray computed tomography is increasingly used technique for the study of the small rodent bones micro-structure. In this part of the work, the trabecular and cortical bone morphology is assessed in the distal half of rat femur. We developed new method for mapping the cortical position and dimensions from a central longitudinal axis with one degree angular resolution. This method was used to examine differences between experimental groups. The bone position in tomographic slices is aligned before the mapping using the propound standardization procedure. The activity of remodelling process of the long bone is studied on the system of cortical canals.
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