Automatic detection and classification of non metallic inclusions in steel via image processing

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

ACTA AGRONOMICA SINICA ◽

10.3724/sp.j.1006.2012.00374 ◽

2013 ◽

Vol 38 (2) ◽

pp. 374-379 ◽

Cited By ~ 3

Author(s):

Zhi-Li PAN ◽

Meng QI ◽

Chun-Yang WEI ◽

Feng LI ◽

Shi-Xiang ZHANG ◽

...

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Support Vector ◽

Tobacco Leaves ◽

Region Classification

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Classification of Red Blood Cell Morphology Using Image Processing and Support Vector Machine

Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology ◽

10.1145/3397391.3397407 ◽

2020 ◽

Author(s):

Paul Daniel C. Divina ◽

John Philip T. Felices ◽

Carlos C. Hortinela ◽

Janette C. Fausto ◽

Flordeliza L. Valiente ◽

...

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

Blood Cell ◽

Cell Morphology ◽

Red Blood Cell ◽

Support Vector ◽

Blood Cell Morphology

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Automated Classification of Alzheimer’s Disease Based on MRI Image Processing using Convolutional Neural Network (CNN) with AlexNet Architecture

Journal of Physics Conference Series ◽

10.1088/1742-6596/1844/1/012020 ◽

2021 ◽

Vol 1844 (1) ◽

pp. 012020

Author(s):

Y N Fu’adah ◽

I Wijayanto ◽

N K C Pratiwi ◽

F F Taliningsih ◽

S Rizal ◽

...

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Image Processing ◽

Alzheimer's Disease ◽

Convolutional Neural Network ◽

Automated Classification ◽

Mri Image

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A tool for finding inclusion clusters in steel SEM specimens

Open Engineering ◽

10.1515/eng-2020-0068 ◽

2020 ◽

Vol 10 (1) ◽

pp. 642-648

Author(s):

Anna-Mari Wartiainen ◽

Markus Harju ◽

Satu Tamminen ◽

Leena Määttä ◽

Tuomas Alatarvas ◽

...

Keyword(s):

Prediction Models ◽

Microscopic Analysis ◽

Steel Specimen ◽

Sem Analysis ◽

Quality Prediction ◽

Automatic Production ◽

Automatic Interpretation ◽

Metallic Inclusions ◽

Purity Analysis ◽

Inclusions In Steel

AbstractNon-metallic inclusions, especially large or clustered inclusions, in steel are usually harmful. Thus, the microscopic analysis of test specimens is an important part of the quality control. This steel purity analysis produces a large amount of individual inclusion information for each test specimen. The interpretation of the results is laborious and the comparison of larger product groups practically impossible. The purpose of this study was to develop an easy-to-use tool for automatic interpretation of the SEM analysis to differentiate clustered and large inclusions information from the manifold individual inclusion information. Because of the large variety of the potential users, the tool needs to be applicable for any steel grade and application, both for liquid and final product specimen, to analyse automatically steel specimen inclusions, especially inclusion clusters, based on the INCA Feature program produced data from SEM/EDS. The developed tool can be used to improve the controlling of the steel purity or for automatic production of new inclusion cluster features that can be utilised further in quality prediction models, for example.

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