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Jan 2015
Last activity
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May 2015

Short Description

The C-CRAFT software enables to segment particles and estimate background in 2D or 3D image sequences. We consider a statistical Bayesian approach in the framework of conditional random fields. Within this approach, we take advantage of a robust detection measure for fluorescence microscopy based on the distribution of neighbor patch similarity. We formulate the vesicle segmentation and background estimation as a global energy minimization problem. An iterative scheme to jointly segment vesicles and background is proposed for 2D-3D fluorescence image sequences.

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Changelog

  • Version 0.0.1.4 • Released on: 2015-05-26 08:44:14
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    Description:

    Class name problem

  • Version 0.0.1.3 • Released on: 2015-05-22 10:09:22
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    Description:

    A few details that were lost in the update process.

  • Version 0.0.1.2 • Released on: 2015-05-22 09:34:15
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    Description:

    New interface, protocol and mass centers exported to the swimming-pool.

  • Version 0.0.1.1 • Released on: 2015-01-16 13:50:21
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  • Version 0.0.1.0 • Released on: 2015-01-16 10:10:32
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