Detection theory approach to multichannel pattern location

We propose and assess new algorithms for detecting and locating an object in multichannel images. These algorithms are optimal for additive Gaussian noise and maximize the likelihood of the observed images. We consider two cases, in which the illumination of the target and the variance of the noise in each channel are either known or unknown. We show that in the latter case the algorithm provides accurate estimates of variance and luminance. These algorithms can be viewed as postprocessed versions of the correlation of a reference with the scene image in each channel

Detection theory approach to multichannel pattern location

We propose and assess new algorithms for detecting and locating an object in multichannel images. These algorithms are optimal for additive Gaussian noise and maximize the likelihood of the observed images. We consider two cases, in which the illumination of the target and the variance of the noise in each channel are either known or unknown. We show that in the latter case the algorithm provides accurate estimates of variance and luminance. These algorithms can be viewed as postprocessed versions of the correlation of a reference with the scene image in each channel

Generic model for the biomolecular organization probed by Second Harmonic Generation polarization microscopy

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Studies on lipid order in cell membranes using fluorescence polarimetric microscopy

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