Summary of Reconstruction Of the Shape Of Irregular Rough Particles From Their Interferometric Images Using a Convolutional Neural Network, by Alexis Abad et al.
Reconstruction of the shape of irregular rough particles from their interferometric images using a convolutional neural network
by Alexis Abad, Alexandre Poux, Alexis Boulet, Marc Brunel
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a convolutional neural network (CNN) that reconstructs the shape of irregular rough particles from their interferometric images. The proposed model uses a UNET architecture with residual block modules and is trained on 18,000 experimental interferometric images using the AUSTRAL supercomputer. The CNN is tested on centrosymmetric and non-centrosymmetric particle shapes, achieving good accuracy in reconstructing the size and 3D orientation of particles. The research also demonstrates the capability to reconstruct the 3D shape of particles from three reconstructed faces using three angles of view. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a computer program that can recognize and recreate the shape of irregular objects from blurry images. The team creates a special type of neural network called a convolutional neural network (CNN) that uses a unique architecture to learn from data. They train the CNN on thousands of pictures taken with a special device that produces images of known shapes. The researchers then test the model by trying it out on new, unseen shapes and angles. The results show that the program is very good at recognizing and recreating the shape of particles. |
Keywords
» Artificial intelligence » Cnn » Neural network » Unet
