On the public citation graph
https://doi.org/10.2139/ssrn.4646874
0 signed · 19 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s00170-015-7514-3openalex-extracted
Feature selection for surface defect classification of extruded aluminum profiles
inferredcites →https://doi.org/10.1016/j.measurement.2021.109185openalex-extracted
A convolutional neural network-based method for workpiece surface defect detection
inferredcites →https://doi.org/10.1016/j.neucom.2020.01.085openalex-extracted
Recent advances in deep learning for object detection
inferredcites →https://doi.org/10.1016/j.neucom.2022.01.005openalex-extracted
Review the state-of-the-art technologies of semantic segmentation based on deep learning
inferredcites →https://doi.org/10.1109/access.2021.3124814openalex-extracted
Edge-Aware Multi-Level Interactive Network for Salient Object Detection of Strip Steel Surface Defects
inferredcites →https://doi.org/10.1109/cisp.2011.6100553openalex-extracted
System design for PCB defects detection based on AOI technology
inferredcites →https://doi.org/10.1109/iros.2017.8206396openalex-extracted
MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes
inferredcites →https://doi.org/10.1109/ivs.2004.1336435openalex-extracted
Defect detection on rail surfaces by a vision based system
inferredcites →https://doi.org/10.1109/tim.2020.3002277openalex-extracted
EDRNet: Encoder–Decoder Residual Network for Salient Object Detection of Strip Steel Surface Defects
inferredcites →https://doi.org/10.1109/tpami.2009.102openalex-extracted
Shape and Spatially-Varying BRDFs from Photometric Stereo
inferredcites →https://doi.org/10.1117/12.7972479openalex-extracted
Photometric Method For Determining Surface Orientation From Multiple Images
inferredcites →https://doi.org/10.1145/3317640.3317661openalex-extracted
Detection Algorithm of Aluminum Surface Defects Using Machine Vision
inferredcites →https://doi.org/10.1162/neco_a_00990openalex-extracted
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
inferredcites →https://doi.org/10.1186/1687-5281-2014-50openalex-extracted
Review of vision-based steel surface inspection systems
inferredcites →https://doi.org/10.3390/app11062606openalex-extracted
An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets
inferredcites →https://doi.org/10.3390/app8091575openalex-extracted
Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
inferredcites →https://doi.org/10.3390/rs14040871openalex-extracted
Deep Learning-Based Change Detection in Remote Sensing Images: A Review
inferredcites →https://doi.org/10.3390/s20061562openalex-extracted
Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network
inferredcites →https://doi.org/10.3390/s22218374openalex-extracted
Photometric-Stereo-Based Defect Detection System for Metal Parts
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.