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Semi-Supervised Learning Framework for Aluminum Alloy Metallographic Image Segmentation

https://doi.org/10.1109/access.2021.3059505
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/jemt.20870openalex-snapshot
Automatic evaluation of nickel alloy secondary phases from SEM images
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Image Processing, Analysis and Machine Vision
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ICNet for Real-Time Semantic Segmentation on High-Resolution Images
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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Support-Vector Networks
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A survey on semi-supervised learning
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Image processing, analysis and machine vision
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Advanced microstructure classification by data mining methods
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Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials
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New methods for automatic quantification of microstructural features using digital image processing
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Grain boundary detection and second phase segmentation based on multi-task learning and generative adversarial network
inferredcites →https://doi.org/10.1016/j.ndteint.2009.05.002openalex-snapshot
Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1038/s41598-018-20037-5openalex-snapshot
Advanced Steel Microstructural Classification by Deep Learning Methods
inferredcites →https://doi.org/10.1038/s41598-018-20438-6openalex-snapshot
Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures
inferredcites →https://doi.org/10.1109/3dv.2016.79openalex-snapshot
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/iccv.2015.203openalex-snapshot
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
inferredcites →https://doi.org/10.1109/tmi.2019.2959609openalex-snapshot
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
inferredcites →https://doi.org/10.1109/tpami.2016.2572683openalex-snapshot
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot
A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01600.xopenalex-snapshot
Maximum Likelihood from Incomplete Data Via the EM Algorithm
inferredcites →https://doi.org/10.3390/sym10040107openalex-snapshot
Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images
inferredcites →https://doi.org/10.3390/sym12040639openalex-snapshot
Microstructure Instance Segmentation from Aluminum Alloy Metallographic Image Using Different Loss Functions
inferredcites →https://doi.org/10.4028/www.scientific.net/amm.152-154.276openalex-snapshot
Metallographical Image Segmentation and Compression
inferredcites →https://doi.org/10.48550/arxiv.1802.06955openalex-snapshot
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1804.03999openalex-snapshot
Attention U-Net: Learning Where to Look for the Pancreas
inferredcites →https://doi.org/10.7551/mitpress/9780262033589.001.0001openalex-snapshot
Semi-Supervised Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges
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Comprehensive overview of machine learning applications in MOFs: from modeling processes to latest applications and design classifications
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inferred← citeshttps://doi.org/10.1109/cvidl70130.2026.11637689openalex-snapshot
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Measurement of Retained Austenite in Gear Steel Microstructure Using a Novel Semi-Supervised Segmentation Framework
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Recent Advancements in Aluminum Alloy Research: Integrating Traditional Metallurgy with Machine Learning and Data-Driven Approaches
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Machine-Learning-Based Automatic Metallographic Grading System for High-Gloss Anodized Aluminum Profiles
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