Machine vision-driven automatic recognition of particle size and morphology in SEM images

Kim, Hyojin; Han, Jinkyu; Han, T. Yong-Jin

doi:10.1039/d0nr04140h

Cited by 59 publications

(39 citation statements)

References 25 publications

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“…Image recognition and mining are widely used, such as judging the growth cycle and growth situation of crops from the acquired images, judging the scale and development direction of urban construction from the acquired images. ere have been certain applications and research in various aspects in image-based recognition, tracking, and mining [6][7][8]. In terms of mechanical operation, image recognition can be used to track mechanical operations, ensure the safety of mechanical production, improve the corresponding mechanical production efficiency, realize the automation of mechanical processing, and improve the level of mechanical manufacturing in the manufacturing industry [5,9,10].…”

Section: Introductionmentioning

confidence: 99%

Digital Image Automatic Recognition and Tracking Based on Electronic Derotation Algorithm

Gao

2021

Advances in Multimedia

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With the continuous development of my country’s social economy, the ways to acquire images have become more and more abundant. How to effectively process, manage, and mine images has become a major and difficult problem in research. In view of the difficult problem of image recognition, the electronic derotation algorithm is introduced in this study, by combing and monitoring the edge features, establishing a corresponding sample database, analyzing the edge features of the image, and performing effective and stable tracking, so as to realize the automatic recognition and tracking of the digital image. The simulation experiment results show that the electronic derotation algorithm is effective and can support the automatic recognition and tracking of digital images.

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Section: Introductionmentioning

confidence: 99%

Digital Image Automatic Recognition and Tracking Based on Electronic Derotation Algorithm

Gao

2021

Advances in Multimedia

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“…More recently, Kim et al have reported an image-recognition tool that identifies the size of nanomaterials and classifies the morphology of each nanomaterial into one of the four categories: nanocubes, nanoparticles, core-shell nanoparticles, and nanorods. 57,130 The particles are located by applying a distance transform-based segmentation process on a binarized form of the image, while their size estimation tracks a similar process to that of ImageDataExtractor. 125 Kim et al identifies and extracted SEM and TEM images from the document via a different route to ImageDataExtractor, 125 employing a convolutional neural network (CNN) with transfer learning.…”

Section: Data Resource(s) Data Summary Example Usagementioning

confidence: 99%

Data-driven materials research enabled by natural language processing and information extraction

Olivetti

Cole

Kim

et al. 2020

Applied Physics Reviews

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178

103

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Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains-enabled by techniques adapted from the field of natural language processing-therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. We discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

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“…[6] developed ImageDataExtractor, a pipeline to automatically mine microscopy images from literature and perform statistical analysis of particle sizes. Kim et al [7] created a tool for the size analysis of nanoparticles in SEM images, distinguishing between two broad classes of nanoparticles: core-only and core-shell nanoparticles. Hiszpanski et al [8] utilized this tool to gather insights from nanoparticle literature.…”

Section: Background and Summarymentioning

confidence: 99%

“…Annotation of ground truth bounding boxes and classes were performed using the LabelImg Past efforts on the task of label and scale detection have mostly used classical thresholdbased segmentation techniques which make use of common attributes such as the rectangular shape and white color of label and scale boxes to identify them [6], [7]. By instead using the YOLOv4 model for this task, we preclude any such prior assumptions on the size, shape, and color of scales and labels.…”

Section: Detection and Interpretation Of Labels Scales And Barsmentioning

confidence: 99%

Dataset of gold nanoparticle sizes and morphologies extracted from literature-mined microscopy images

Subramanian¹,

Cruse²,

Trewartha³

et al. 2021

Preprint

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The factors controlling the size and morphology of nanoparticles have so far been poorly understood. Data-driven techniques are an exciting avenue to explore this field through the identification of trends and correlations in data. However, for these techniques to be utilized, large datasets annotated with the structural attributes of nanoparticles are required. While experimental SEM/TEM images collected from controlled experiments are reliable sources of this information, large-scale collection of these images across a variety of experimental conditions is expensive and infeasible.Published scientific literature, which provides a vast source of high-quality figures including SEM/TEM images, can provide a large amount of data at a lower cost if effectively mined. In this work, we develop an automated pipeline to retrieve and analyse microscopy images from gold nanoparticle literature and provide a dataset of 4361 SEM/TEM images of gold nanoparticles along with automatically extracted size and morphology information. The dataset can be queried to obtain information about the physical attributes of gold nanoparticles and their statistical distributions.

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Machine vision-driven automatic recognition of particle size and morphology in SEM images

Abstract: A comprehensive framework to automatically perform size and morphology recognition of nanoparticles in SEM images in a high-throughput manner.

Cited by 59 publications

References 25 publications

Digital Image Automatic Recognition and Tracking Based on Electronic Derotation Algorithm

Digital Image Automatic Recognition and Tracking Based on Electronic Derotation Algorithm

Data-driven materials research enabled by natural language processing and information extraction

Dataset of gold nanoparticle sizes and morphologies extracted from literature-mined microscopy images

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