Development and implementation of grain boundary identification algorithms in steels after melting and controlled cooling
PBN-AR
Instytucja
Wydział Metali Nieżelaznych (Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie)
Książka
Tytuł książki
Metal 2017. 26\textsuperscript{th} international conference on Metallurgy and materials : May 24\textsuperscript{th}–26\textsuperscript{th} 2017, Brno, Czech Republic, EU : abstracts
Data publikacji
2017
ISBN
978-80-87294-73-4
Wydawca
TANGER Ltd.
Publikacja
Główny język publikacji
EN
Tytuł rozdziału
Development and implementation of grain boundary identification algorithms in steels after melting and controlled cooling
Rok publikacji
2017
Strony (od-do)
210--215
Numer rozdziału
Link do pełnego tekstu
Identyfikator DOI
Liczba arkuszy
0.42
Hasło encyklopedyczne
Autorzy
(liczba autorów: 4)
Pozostali autorzy
+ 2
Słowa kluczowe
EN
Python
grain boundary
microstructure images recognition
images recognition algorithms
Konferencja
Indeksowana w Scopus
tak
Indeksowana w Web of Science Core Collection
tak
Liczba cytowań z Web of Science Core Collection
Nazwa konferencji (skrócona)
Metal 2017
Nazwa konferencji
26th International Conference on Metallurgy and Materials
Początek konferencji
2017-05-24
Koniec konferencji
2017-05-26
Lokalizacja konferencji
Brno
Kraj konferencji
CZ
Lista innych baz czasopism i abstraktów w których była indeksowana
Streszczenia
Język
EN
Treść
Current approach for microstructure images recognition and image-based properties measurement utilized manual iterative image filtering and threshold-binarization process until image areas, corresponding to desired structure elements, were extracted. This approach, however, led to information loss and inaccurate results, due to extensive filtering required to remove noise and extract features. Results were very sensitive to scanning method used to obtain images, image quality and coloring. Also, manual binarization process assumes that desired structure features are already known. In this article, we present and describe implementation and results of new approach, where image is segmented using algorithms based on Watershed [1], Morphological Geodesic Active Contours (MorphGAC), and Morphological Active Contours without Edges (MorphACWE) [2-3] algorithms, providing contextindependent image partitioning. After image is segmented, obtained segments are classified and then measurements are taken for desired classes. This approach allows to find more features than binarization approach with higher accuracy, as minimal filtering is required, and MorphGAC/ACWE algorithms tend to be more accurate in edge and contour detection than simple thresholding or linear filters. Our program is written in Python, with use of OpenCV and Scikit-image libraries. It implements mentioned algorithm and provides tools for image filtering, and also analysis and measurements tools including features size and distribution statistics are available. For optimization enhancements, Python C-extensions and OpenCL-based GPU processing will be used if needed. Future enhancements include graph theory structure analysis, as image partitioned into segments corresponding to structure elements can be easily represented in a graph form. Our goal is also to utilize neural network for microstructure recognition, segmentation and property analysis. © 2017 TANGER Ltd., Ostrava.
Cechy publikacji
chapter-in-a-book
peer-reviewed
Inne
System-identifier
idp:105968