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Found 9 papers, 5 papers with code
Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction
Most modern fingerprint authentication systems rely on contact-based fingerprints, which require the use of fingerprint scanners or fingerprint sensors for capturing fingerprints during the authentication process.
Deep Age-Invariant Fingerprint Segmentation System
However, segmenting or auto-localizing all fingerprints in a slap image is a challenging task due to the different orientations of fingerprints, noisy backgrounds, and the smaller size of fingertip components.
Deep Slap Fingerprint Segmentation for Juveniles and Adults
Subsequently, the dataset is used to evaluate the matching performance of the NFSEG, a slap fingerprint segmentation system developed by NIST, on slaps from adults and juvenile subjects.
Pulmonary Disease Classification Using Globally Correlated Maximum Likelihood: an Auxiliary Attention mechanism for Convolutional Neural Networks
Two complementary generalization properties of CNNs, translation invariance and equivariance, are particularly useful in detecting manifested abnormalities associated with pulmonary disease, regardless of their spatial locations within the image.
Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia
Trust in predictions made by machine learning models is increased if the model generalizes well on previously unseen samples and when inference is accompanied by cogent explanations of the reasoning behind predictions.
FlexServe: Deployment of PyTorch Models as Flexible REST Endpoints
The integration of artificial intelligence capabilities into modern software systems is increasingly being simplified through the use of cloud-based machine learning services and representational state transfer architecture design.
The Utility of Feature Reuse: Transfer Learning in Data-Starved Regimes
The use of transfer learning with deep neural networks has increasingly become widespread for deploying well-tested computer vision systems to newer domains, especially those with limited datasets.
Hazard Detection in Supermarkets using Deep Learning on the Edge
Supermarkets need to ensure clean and safe environments for both shoppers and employees.
Machine Learning at the Network Edge: A Survey
To address this issue, efforts have been made to place additional computing devices at the edge of the network, i. e close to the IoT devices where the data is generated.
