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Object detection and automatic measurements with a data-driven algorithm for marked point process optimization

Abstract : This paper deals with object detection and automatic measurements from optical microscopy and scanning electron microscopy (SEM) 2-dimensional images. More specifically, we consider the problem of detecting and measuring circular cells and rectilinear thick fibers. We adopt the spatial point process viewpoint and follow the maximum a posteriori (MAP) principle to obtain an optimal object configuration. We design a novel data-driven version of the data term in the MAP criterion in the fiber case, of the reference spatial marked point process and of the Markov Chain Monte-Carlo (MCMC) sampler, both in the cell case and in the fiber case. To this purpose, information from the image under consideration is incorporated by means of either the Radon transform or the circular Hough transform. We apply the proposed method to a real cell optical microscopy image and to a real fiber SEM image. Our results show that the proposed algorithm is fast, reliable and stable. We also produce the required characterization of the object morphology.
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https://hal.archives-ouvertes.fr/hal-00975622
Contributor : Claire Coiffard Marre <>
Submitted on : Thursday, April 10, 2014 - 1:46:45 PM
Last modification on : Thursday, July 9, 2020 - 8:12:28 AM
Long-term archiving on: : Thursday, July 10, 2014 - 12:11:20 PM

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  • HAL Id : hal-00975622, version 2

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Claire Coiffard Marre, Ségolen Geffray. Object detection and automatic measurements with a data-driven algorithm for marked point process optimization. 2014. ⟨hal-00975622v2⟩

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