Summary of Houghtoradon Transform: New Neural Network Layer For Features Improvement in Projection Space, by Alexandra Zhabitskaya et al.
HoughToRadon Transform: New Neural Network Layer for Features Improvement in Projection Space
by Alexandra Zhabitskaya, Alexander Sheshkus, Vladimir L. Arlazarov
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 paper introduces the HoughToRadon Transform layer, a novel neural network component designed to improve the speed of semantic image segmentation models. The layer modifies feature maps produced by Hough Transform, allowing for more efficient processing and linear parameter space in terms of angle and shift. By adjusting two new parameters, users can balance processing time and quality. Experiments on the MIDV-500 dataset show a 97.7% accuracy rate, outperforming other models with higher computational complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it faster to separate images into meaningful parts using computers. It creates a special layer that helps make neural networks work better for this task. The new layer takes features from an existing process called Hough Transform and makes them easier to use. This lets the computer work more efficiently, making tasks like document analysis go faster and more accurate. |
Keywords
* Artificial intelligence * Image segmentation * Neural network
