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Data-Driven Image Segmentation of Complex Microstructures with Deep Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-Driven Image Segmentation of Complex Microstructures with Deep Learning./
作者:	Lei, Bo.
面頁冊數:	1 online resource (143 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-06, Section: B.
Contained By:	Dissertations Abstracts International84-06B.
標題:	Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30242403click for full text (PQDT)
ISBN:	9798363516214

Data-Driven Image Segmentation of Complex Microstructures with Deep Learning.
Lei, Bo.

Data-Driven Image Segmentation of Complex Microstructures with Deep Learning. - 1 online resource (143 pages)

Source: Dissertations Abstracts International, Volume: 84-06, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2022.

Includes bibliographical references

In quantitative microscopy, image segmentation plays a central role in quantitative measurements and analyses of microstructure constituents. Developing effective and efficient methods for automating the segmentation process is highly valued for materials research and manufacturing. Conventional image processing-based segmentation methods reach their limits in handling complicated microstructures and often require sophisticated processing pipelines. The cutting-edge data-driven deep learning methods have made huge breakthroughs in image-based tasks and they provide new possibilities for advanced microstructure image segmentation.In this thesis, we demonstrate that deep learning methods can be successfully applied to microstructure image segmentation tasks with great advantages in automation, performance and generality. We demonstrate the capabilities of deep learning methods in two different problems: (1) segmentation of complex multi-constituents microstructures in ultrahigh carbon steel and (2) segmentation of low-contrast lath-shaped bainite in complex phase steel. General guidelines and strategies for tackling such tasks are discussed.To alleviate annotation cost and achieve high efficiency for practical applications, we develop our deep learning models in a semi-supervised manner with a significantly reduced amount of annotated data. An automated training image selection algorithm is proposed and we demonstrate in two steel microstructure segmentation datasets that deep learning models trained by one or a few images are competitive with fully-supervised models using 4 times more training images.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798363516214Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

542853
Electronic books.

Data-Driven Image Segmentation of Complex Microstructures with Deep Learning.
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