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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.17064 |
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| _version_ | 1866910540126748672 |
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| author | Müller, Simone Kolb, Daniel Müller, Matthias Kranzlmüller, Dieter |
| author_facet | Müller, Simone Kolb, Daniel Müller, Matthias Kranzlmüller, Dieter |
| contents | Learning-based analysis of images is commonly used in the fields of mobility and robotics for safe environmental motion and interaction. This requires not only object recognition but also the assignment of certain properties to them. With the help of this information, causally related actions can be adapted to different circumstances. Such logical interactions can be optimized by recognizing object-assigned properties. Density as a physical property offers the possibility to recognize how heavy an object is, which material it is made of, which forces are at work, and consequently which influence it has on its environment. Our approach introduces an AI-based concept for assigning physical properties to objects through the use of associated images. Based on synthesized data, we derive specific patterns from 2D images using a neural network to extract further information such as volume, material, or density. Accordingly, we discuss the possibilities of property-based feature extraction to improve causally related logics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17064 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-based Density Recognition Müller, Simone Kolb, Daniel Müller, Matthias Kranzlmüller, Dieter Computer Vision and Pattern Recognition Learning-based analysis of images is commonly used in the fields of mobility and robotics for safe environmental motion and interaction. This requires not only object recognition but also the assignment of certain properties to them. With the help of this information, causally related actions can be adapted to different circumstances. Such logical interactions can be optimized by recognizing object-assigned properties. Density as a physical property offers the possibility to recognize how heavy an object is, which material it is made of, which forces are at work, and consequently which influence it has on its environment. Our approach introduces an AI-based concept for assigning physical properties to objects through the use of associated images. Based on synthesized data, we derive specific patterns from 2D images using a neural network to extract further information such as volume, material, or density. Accordingly, we discuss the possibilities of property-based feature extraction to improve causally related logics. |
| title | AI-based Density Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.17064 |