Temat: geometria obliczeniowa - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Generating placated random shapes for an area estimation study
Autorzy:: Almowanes, A.
Kakiashvili, T.
Koczkodaj, W. W.
Powiązania:: https://bibliotekanauki.pl/articles/123002.pdf
Data publikacji:: 2013
Wydawca:: Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej
Tematy:: image processing
Gaussian blur
random polygon
computational geometry
cognitive process
software testing
przetwarzanie obrazów
geometria obliczeniowa
proces poznawczy
testowanie oprogramowania
Opis:: Random but visually nice shapes are often needed for cognitive experiments and processes. This study describes a heuristic for generating random but nice shapes. We call them placated shapes. These shapes are produced by applying the Gaussian blur to randomly generated polygons. Subsequently, the threshold is set to transform pixels to black and white from different shades of gray. This transformation produces placated shapes for easier estimation of areas. Randomly generated placated shapes are used for testing the accuracy of cognitive processes by pairwise comparisons. They can also be used in many other areas such as computer games or software testing. Such shapes could also be used for camouflaging heavy army equipment.
Źródło:: Journal of Applied Mathematics and Computational Mechanics; 2013, 12, 2; 5-13
2299-9965
Pojawia się w:: Journal of Applied Mathematics and Computational Mechanics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Implementation and evaluation of medical imaging techniques based on conformal geometric algebra
Autorzy:: Franchini, Silvia
Gentile, Antonio
Vassallo, Giorgio
Vitabile, Salvatore
Powiązania:: https://bibliotekanauki.pl/articles/329970.pdf
Data publikacji:: 2020
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: medical image segmentation
medical image registration
computational geometry
Clifford algebra
conformal geometric algebra
segmentacja obrazu
rejestracja obrazu medycznego
geometria obliczeniowa
algebra Clifforda
Opis:: Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10^-5.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2020, 30, 3; 415-433
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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