Summary of Mutual Information Calculation on Different Appearances, by Jiecheng Liao et al.
Mutual Information calculation on different appearances
by Jiecheng Liao, Junhao Lu, Jeff Ji, Jiacheng He
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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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 The proposed research applies the concept of mutual information to image alignment and matching tasks, leveraging its ability to measure statistical dependencies between images from different modalities. The approach considers both pixel intensities and spatial relationships, demonstrating its effectiveness in evaluating similarity between images. In this project, the authors apply the mutual information formula to image matching, calculating the similarity between moving and target objects. For comparison, entropy and information-gain methods are also employed to test dependencies. The impact of different environments on mutual information is investigated, with experiments and plots used to illustrate the findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research uses a statistical measure called mutual information to match images from different sources. This helps find similarities between pictures, even if they’re taken in different ways (like CT and MRI scans). The method looks at both what’s inside the images and how the things are arranged. It’s good for comparing images, like matching an object in one picture with another. The researchers compare their approach to other methods that do similar tasks. |
Keywords
» Artificial intelligence » Alignment
