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Data science for nano image analysis

Ding, Yu.

Data science for nano image analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science for nano image analysisby Chiwoo Park, Yu Ding.
Author:	Park, Chiwoo.
other author:	Ding, Yu.
Published:	Cham :Springer International Publishing :2021.
Description:	xvi, 368 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Image analysisData processing.
Online resource:	https://doi.org/10.1007/978-3-030-72822-9
ISBN:	9783030728229$q(electronic bk.)

Data science for nano image analysis
Park, Chiwoo.

Data science for nano image analysis[electronic resource] /by Chiwoo Park, Yu Ding. - Cham :Springer International Publishing :2021. - xvi, 368 p. :ill., digital ;24 cm. - International series in operations research & management science,v.3080884-8289 ;. - International series in operations research & management science ;v. 140..

Chapter 1. Introduction -- Chapter 2. Image Representation -- Chapter 3. Segmentation -- Chapter 4. Shape Analysis -- Chapter 5. Location and Dispersion Analysis -- Chapter 6. Lattice Pattern Analysis -- Chapter 7. Change Point Detection -- Chapter 8. State Space Modeling for Size Changes -- Chapter 9. Shape Change Tracking -- Chapter 10. Tracking Nucleation, Growth and Aggregation -- Chapter 11. Further Issues and Discussions.

This book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type of nano material problems can be better solved by which set of data science methods. The majority of material science research is thus far carried out by domain-specific experts in material engineering, chemistry/chemical engineering, and mechanical & aerospace engineering. The book could benefit materials scientists and manufacturing engineers who were not exposed to systematic data science training while in schools, or data scientists in computer science or statistics disciplines who want to work on material image problems or contribute to materials discovery and optimization. This book provides in-depth discussions of how data science and operations research methods can help and improve nano image analysis, automating the otherwise manual and time-consuming operations for material engineering and enhancing decision making for nano material exploration. A broad set of data science methods are covered, including the representations of images, shape analysis, image pattern analysis, and analysis of streaming images, change points detection, graphical methods, and real-time dynamic modeling and object tracking. The data science methods are described in the context of nano image applications, with specific material science case studies.

ISBN: 9783030728229$q(electronic bk.)

Standard No.: 10.1007/978-3-030-72822-9doiSubjects--Topical Terms:

217056
Image analysis
--Data processing.

LC Class. No.: TA1637 / .P37 2021

Dewey Class. No.: 621.367

Data science for nano image analysis
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505 0 $a Chapter 1. Introduction -- Chapter 2. Image Representation -- Chapter 3. Segmentation -- Chapter 4. Shape Analysis -- Chapter 5. Location and Dispersion Analysis -- Chapter 6. Lattice Pattern Analysis -- Chapter 7. Change Point Detection -- Chapter 8. State Space Modeling for Size Changes -- Chapter 9. Shape Change Tracking -- Chapter 10. Tracking Nucleation, Growth and Aggregation -- Chapter 11. Further Issues and Discussions.
520 $a This book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type of nano material problems can be better solved by which set of data science methods. The majority of material science research is thus far carried out by domain-specific experts in material engineering, chemistry/chemical engineering, and mechanical & aerospace engineering. The book could benefit materials scientists and manufacturing engineers who were not exposed to systematic data science training while in schools, or data scientists in computer science or statistics disciplines who want to work on material image problems or contribute to materials discovery and optimization. This book provides in-depth discussions of how data science and operations research methods can help and improve nano image analysis, automating the otherwise manual and time-consuming operations for material engineering and enhancing decision making for nano material exploration. A broad set of data science methods are covered, including the representations of images, shape analysis, image pattern analysis, and analysis of streaming images, change points detection, graphical methods, and real-time dynamic modeling and object tracking. The data science methods are described in the context of nano image applications, with specific material science case studies.
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