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Found 12 papers, 2 papers with code
Synth4Seg -- Learning Defect Data Synthesis for Defect Segmentation using Bi-level Optimization
Synthetic defect data generation is a popular approach for mitigating data challenges.
VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges.
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection
In this paper, we propose a Robust GAN-inversion (RGI) method with a provable robustness guarantee to achieve image restoration under unknown \textit{gross} corruptions, where a small fraction of pixels are completely corrupted.
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation
To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation.
Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey
Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process.
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data
Unstructured point clouds with varying sizes are increasingly acquired in a variety of environments through laser triangulation or Light Detection and Ranging (LiDAR).
Compressed Smooth Sparse Decomposition
The proposed method, named Compressed Smooth Sparse Decomposition (CSSD), is a one-step method that unifies the compressive image acquisition and decomposition-based image processing techniques.
Additive Tensor Decomposition Considering Structural Data Information
In this paper, we provide a new definition of structural information in tensor data.
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage
In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited training samples.
AKM$^2$D : An Adaptive Framework for Online Sensing and Anomaly Quantification
In point-based sensing systems such as coordinate measuring machines (CMM) and laser ultrasonics where complete sensing is impractical due to the high sensing time and cost, adaptive sensing through a systematic exploration is vital for online inspection and anomaly quantification.
Multiple profiles sensor-based monitoring and anomaly detection
However, there are several challenges in developing an effective process monitoring system: (i) data streams generated by multiple sensors are high-dimensional profiles; (ii) sensor signals are affected by noise due to system-inherent variations; (iii) signals of different sensors have cluster-wise features; and (iv) an anomaly may cause only sparse changes of sensor signals.
Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning
Multivariate functional data from a complex system are naturally high-dimensional and have complex cross-correlation structure.
