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Found 8 papers, 2 papers with code
Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study
Within (semi-)automated visual inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery.
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study
Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery.
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Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System
However, in real-world settings, patients often suffer from loss of vision on time scales of years despite therapy, whereas the prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data.
Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning
However, one problem in the domain is that the faults are often very small and have to be detected within a larger size of the chip or even the wafer.
Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generation
Whereas conventional state-of-the-art image processing systems of recording and output devices almost exclusively utilize square arranged methods, biological models, however, suggest an alternative, evolutionarily-based structure.
A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks
Consisting of stacked hybrid convolutional neural networks (SH-CNN) and inspired by current approaches of visual attention, the realized system draws the focus over the level of detail from its structures to more task-relevant areas of interest.
Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework
Inspired by the human visual perception system, hexagonal image processing in the context of machine learning deals with the development of image processing systems that combine the advantages of evolutionary motivated structures based on biological models.
Recognizing Birds from Sound - The 2018 BirdCLEF Baseline System
Reliable identification of bird species in recorded audio files would be a transformative tool for researchers, conservation biologists, and birders.
