On the public citation graph

Speeding Up the Annotation Process in Semantic Segmentation Industrial Applications

https://doi.org/10.2139/ssrn.4902305
0 signed · 26 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-01234-2_49openalex-snapshot
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
inferredcites →https://doi.org/10.1007/s11661-020-06008-4openalex-snapshot
Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis
inferredcites →https://doi.org/10.1007/s11760-020-01778-1openalex-snapshot
Online learning method based on support vector machine for metallographic image segmentation
inferredcites →https://doi.org/10.1016/j.engappai.2023.106299openalex-snapshot
Exploring the trade-off between performance and annotation complexity in semantic segmentation
inferredcites →https://doi.org/10.1016/j.inffus.2021.09.018openalex-snapshot
A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges
inferredcites →https://doi.org/10.1016/j.patrec.2008.04.005openalex-snapshot
Semantic object classes in video: A high-definition ground truth database
inferredcites →https://doi.org/10.1017/s1431927618015635openalex-snapshot
High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel
inferredcites →https://doi.org/10.1023/b:visi.0000022288.19776.77openalex-snapshot
Efficient Graph-Based Image Segmentation
inferredcites →https://doi.org/10.1038/s41598-019-49105-0openalex-snapshot
Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels
inferredcites →https://doi.org/10.1038/s41598-020-74935-8openalex-snapshot
Unsupervised microstructure segmentation by mimicking metallurgists’ approach to pattern recognition
inferredcites →https://doi.org/10.1109/access.2017.2756872openalex-snapshot
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
inferredcites →https://doi.org/10.1109/access.2021.3059505openalex-snapshot
Semi-Supervised Learning Framework for Aluminum Alloy Metallographic Image Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.350openalex-snapshot
The Cityscapes Dataset for Semantic Urban Scene Understanding
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01652openalex-snapshot
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
inferredcites →https://doi.org/10.1109/cvprw.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/icassp.2018.8462533openalex-snapshot
Unsupervised Image Segmentation by Backpropagation
inferredcites →https://doi.org/10.1109/icccnt49239.2020.9225642openalex-snapshot
Machine Learning Algorithms in Healthcare: A Literature Survey
inferredcites →https://doi.org/10.1109/iccv.2019.00743openalex-snapshot
SegSort: Segmentation by Discriminative Sorting of Segments
inferredcites →https://doi.org/10.1109/iccv.2019.00996openalex-snapshot
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot
A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.1117/1.jei.28.3.033035openalex-snapshot
Aluminum alloy microstructural segmentation method based on simple noniterative clustering and adaptive density-based spatial clustering of applications with noise
inferredcites →https://doi.org/10.1145/3411764.3445518openalex-snapshot
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
inferredcites →https://doi.org/10.2307/2346830openalex-snapshot
Algorithm AS 136: A K-Means Clustering Algorithm
inferredcites →https://doi.org/10.48550/arxiv.2103.14749openalex-snapshot
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
inferredcites →https://doi.org/10.6688/jise.2001.17.5.1openalex-snapshot
A fast algorithm for multilevel thresholding
inferredcites →https://doi.org/10.7208/chicago/9780226613475.003.0021openalex-snapshot
The Impact of Machine Learning on Economics
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.