Publication: Research - peer-review › Article in proceedings – Annual report year: 2011
We provide a deformable model for particle analysis. We in- vestigate particle images from a backlit microscope system where parti- cles suer from out-of-focus blur. The blur is a result of particles being in front or behind the focus plane, and the out-of-focus gives a bias towards overestimating the particle size. This can be handled by only including the particles in focus, but most of the depicted particles will be left out of the analysis, which weakens the statistical estimate of the monitored process. We propose a new method for particle analysis. The model in- corporates particle shape, size and intensity, which enables an estimation of the out-of-focus blur of the particle. Using the particle model param- eters in a regression model we are able to infer 3D information about individual particles. Based on the defocus information we are able to infer the true size and shape of the particles. We demonstrate the capa- bilities of our model on both real and simulated data, and our approach shows promising results for a reliable particle analysis. The potential is more process information obtained over shorter sampling time.
|Title||Computer Vision, Imaging and Computer Graphics. Theory and Applications : Computer Vision, Imaging and Computer Graphics. Theory and Applications Computer Vision, Imaging and Computer Graphics. Theory and Applications International Joint Conference, VISIGRAPP 2010, Angers, France, May 17-21, 2010|
|Conference||International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2010)|
|Period||17-05-10 → 21-05-10|
|Name||Communications in Computer and Information Science|
|Citations||Web of Science® Times Cited: No match on DOI|
- Depth estimation, Particle analysis, Deconvolution, Micro- scopic imaging
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