Particle identification in camera image sensors using computer vision

  title={Particle identification in camera image sensors using computer vision},
  author={Miles Winter and James Bourbeau and Silvia Bravo and Felipe Campos and Matthew Meehan and Jeffrey Peacock and Tyler H. Ruggles and Cassidy Schneider and Ariel Levi Simons and Justin Vandenbroucke},
  journal={Astroparticle Physics},
Recognition of Cosmic Ray Images Obtained from CMOS Sensors Used in Mobile Phones by Approximation of Uncertain Class Assignment with Deep Convolutional Neural Network
The convolutional neural network (CNN)-based approach to the problems of categorization and artefact reduction of cosmic ray images obtained from CMOS sensors used in mobile phones is described and performed on the largest freely available cosmic ray hit dataset.
Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors
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Towards A Global Cosmic Ray Sensor Network: CREDO Detector as the First Open-Source Mobile Application Enabling Detection of Penetrating Radiation
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CNN-Based Classifier as an Offline Trigger for the CREDO Experiment
To tag the artefacts appearing in the CREDO database with a CNN-based trigger, the method based on machine learning allows eliminating the manual supervision of the competition process.
Particle Scrap Reduction of an Automotive Camera Product By Lean Six Sigma DMAIC Approach
In today's world of automotive, one of the key innovative features and technology advancement that we could see on automobiles or cars is by having an external or internal camera or automotive
Human-machine-learning integration and task allocation in citizen science
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Resource Letter MDS-1: Mobile devices and sensors for physics teaching
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Linear Derivative-Based Approach for Data Classification
Automated Examination Grading Using Deep Learning Categorization Techniques


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