On knot detection via picture recognition
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916995904045056 |
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| author | Dranowski, Anne Kabkov, Yura Tubbenhauer, Daniel |
| author_facet | Dranowski, Anne Kabkov, Yura Tubbenhauer, Daniel |
| contents | Our goal is to one day take a photo of a knot and have a phone automatically recognize it. In this expository work, we explain a strategy to approximate this goal, using a mixture of modern machine learning methods (in particular convolutional neural networks and transformers for image recognition) and traditional algorithms (to compute quantum invariants like the Jones polynomial). We present simple baselines that predict crossing number directly from images, showing that even lightweight CNN and transformer architectures can recover meaningful structural information. The longer-term aim is to combine these perception modules with symbolic reconstruction into planar diagram (PD) codes, enabling downstream invariant computation for robust knot classification. This two-stage approach highlights the complementarity between machine learning, which handles noisy visual data, and invariants, which enforce rigorous topological distinctions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_06284 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On knot detection via picture recognition Dranowski, Anne Kabkov, Yura Tubbenhauer, Daniel Machine Learning Computer Vision and Pattern Recognition Geometric Topology Primary: 57K10, 68T07, secondary: 57K14, 68T45 Our goal is to one day take a photo of a knot and have a phone automatically recognize it. In this expository work, we explain a strategy to approximate this goal, using a mixture of modern machine learning methods (in particular convolutional neural networks and transformers for image recognition) and traditional algorithms (to compute quantum invariants like the Jones polynomial). We present simple baselines that predict crossing number directly from images, showing that even lightweight CNN and transformer architectures can recover meaningful structural information. The longer-term aim is to combine these perception modules with symbolic reconstruction into planar diagram (PD) codes, enabling downstream invariant computation for robust knot classification. This two-stage approach highlights the complementarity between machine learning, which handles noisy visual data, and invariants, which enforce rigorous topological distinctions. |
| title | On knot detection via picture recognition |
| topic | Machine Learning Computer Vision and Pattern Recognition Geometric Topology Primary: 57K10, 68T07, secondary: 57K14, 68T45 |
| url | https://arxiv.org/abs/2510.06284 |