Extracting 3D parametric curves from 2D images of Helical objects
| Autor: | Chris G. Willcocks, Carl J. Nelson, Boguslaw Obara, Philip T. Jackson |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
2d images
Ground truth business.industry Applied Mathematics 020207 software engineering 02 engineering and technology Tortuosity Hausdorff distance Computational Theory and Mathematics Artificial Intelligence Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Image noise 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence Parametric equation business Algorithm Software Image object Mathematics |
| Zdroj: | IEEE transactions on pattern analysis and machine intelligence, 2016, Vol.39(9), pp.1757-1769 [Peer Reviewed Journal] |
| ISSN: | 0162-8828 |
| Popis: | Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively. |
| Databáze: | OpenAIRE |
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