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A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction from Images

A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction from Images

Maria Kulikova, Ian Jermyn, Xavier Descombes, Elena Zhizhina, Josiane Zerubia
Copyright: © 2013 |Pages: 12
ISBN13: 9781466639065|ISBN10: 1466639067|EISBN13: 9781466639072
DOI: 10.4018/978-1-4666-3906-5.ch006
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MLA

Kulikova, Maria, et al. "A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction from Images." Intelligent Computer Vision and Image Processing: Innovation, Application, and Design, edited by Muhammad Sarfraz, IGI Global, 2013, pp. 71-82. https://doi.org/10.4018/978-1-4666-3906-5.ch006

APA

Kulikova, M., Jermyn, I., Descombes, X., Zhizhina, E., & Zerubia, J. (2013). A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction from Images. In M. Sarfraz (Ed.), Intelligent Computer Vision and Image Processing: Innovation, Application, and Design (pp. 71-82). IGI Global. https://doi.org/10.4018/978-1-4666-3906-5.ch006

Chicago

Kulikova, Maria, et al. "A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction from Images." In Intelligent Computer Vision and Image Processing: Innovation, Application, and Design, edited by Muhammad Sarfraz, 71-82. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3906-5.ch006

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Abstract

Object extraction from images is one of the most important tasks in remote sensing image analysis. For accurate extraction from very high resolution (VHR) images, object geometry needs to be taken into account. A method for incorporating strong yet flexible prior shape information into a marked point process model for the extraction of multiple objects of complex shape is presented. To control the computational complexity, the objects considered are defined using the image data and the prior shape information. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process on the space of multiple objects. The authors present several experimental results on the extraction of tree crowns from VHR aerial images.

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