Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
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Recently, micro-rotation confocal microscopy has enabled the acquisition of a sequence of slices for a non-adherent living cells where the slices' positions are roughly controlled by a dielectric-field biological cage. The high resolution volume reconstruction requires then the integration of precise alignment of slice positions. We propose in the Bayesian context, a new method combining both slice positioning and 3D volume reconstruction simultaneously, which leads naturally to an energy minimization procedure of a variational problem. An automatic calibration paradigm via Maximum Likelihood estimation (MLE) principle is used for the relative hyper-parameter determination. We provide finally experimental comparison results on both conventional z-stack confocal images and 3D volume reconstruction from micro-rotation slices of the same non-adherent living cell to show its potential biomedical application.