Detection theory approach to multichannel pattern location

Abstract

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.

Multi-Channel Planet Detection Algorithm for Angular Differential Imaging

“…A relatively simple one is to (a) resample the difference images i d n (x, y) onto a polar grid so as to obtain "de-warped images" i d n (ρ, θ) in which the trajectory of a planet is linear, and (b) to perform a multi-channel matched filtering of these de-warped images. The multi-channel matched filter, as analyzed by Guillaume et al [3], is derived from detection theory and is the Maximum-Likelihood estimator for the planet position if the noise is stationary white Gaussian.…”

Pattern recognition with a digital holographic microscope working in partially coherent illumination

“…The ASFS algorithm that has been developed to compute correlation filter for image recognition 33 can be applied for complex amplitude patterns obtained in digital holography with only a few changes. As for many other correlation algorithms, the ASFS filter is computed thanks to a set of typical patterns that have to be representative of the application.…”

“…32 Critical reduction of the correlation height occurs when the targets are surrounded by noise or by clutter. This problem has been considered and elegant solutions based on the decision theory 33,34 or on a minimization of mean square error 35 have been proposed. To make the recognition insensitive to the input distortions, we developed the automatic spatial frequency selection ͑ASFS͒ algorithm that involves reference images and distorted versions of them to select the significant spatial frequencies 36 -38 in the recognition process.…”