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Ellipse Motion Estimation Using Parametric Snakes

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Abstract

In this paper we propose a multiscale parametric snake model for ellipse motion estimation across a sequence of images. We use a robust ellipse parameterization based on the geometry of the intersection of a cylinder and a plane. The ellipse parameters are optimized in each frame by searching for local minima of the snake model energy including temporal coherence in the ellipse motion. One advantage of this method is that it just considers the convolution of the image with a Gaussian kernel and its gradient, and no edge detection is required. A detailed study about the numerical evaluation of the snake energy on ellipses is presented. We propose a Newton–Raphson-type algorithm to estimate a local minimum of the energy. We present some experimental results on synthetic data, real video sequences and 3D medical images.

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Notes

  1. We use the implementation of this method available at goo.gl.

  2. We use the implementation of this method available at bigwww.epfl.ch.

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Acknowledgements

This research has partially been supported by the MIN- ECO Projects References TIN2016-76373-P (AEI/FED- ER, UE) and MTM2016-75339-P (AEI/FEDER, UE) (Ministerio de Economía y Competitividad, Spain).

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Authors and Affiliations

  1. Centro de Tecnologías de la Imagen (CTIM), Universidad de Las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Palmas de Gran Canaria, Spain

    Luis Alvarez, Esther González, Carmelo Cuenca & Agustín Trujillo

  2. Centro Singular de Investigación en Tecnoloxías da Información (CITIUS), University of Santiago de Compostela, Santiago de Compostela, Spain

    Pablo G. Tahoces

  3. University Hospital Complex of Santiago de Compostela (CHUS), Santiago de Compostela, Spain

    José M. Carreira

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  1. Luis Alvarez
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  2. Esther González
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  3. Carmelo Cuenca
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  4. Agustín Trujillo
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  5. Pablo G. Tahoces
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Correspondence to Luis Alvarez.

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Alvarez, L., González, E., Cuenca, C. et al. Ellipse Motion Estimation Using Parametric Snakes. J Math Imaging Vis 60, 1095–1110 (2018). https://doi.org/10.1007/s10851-018-0798-9

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