6533b863fe1ef96bd12c78a9
RESEARCH PRODUCT
Maximum likelihood for target location in the presence of substitutive noise .
Henri H. ArsenaultCarlos FerreiraPhilippe RéfrégierPascuala García-martínezsubject
Pixelbusiness.industryMaterials Science (miscellaneous)Binary numberImage processing02 engineering and technologyFunction (mathematics)021001 nanoscience & nanotechnology01 natural sciencesIndustrial and Manufacturing EngineeringImage (mathematics)010309 opticsNoiseOpticsComputer Science::Computer Vision and Pattern Recognition[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]Computer Science::Multimedia0103 physical sciencesPattern recognition (psychology)Business and International Management0210 nano-technologybusinessAlgorithmLinear filterMathematicsdescription
We consider the optimal likelihood algorithm for the estimation of a target location when the images are corrupted by substitutive noise. We show the relationship between the optimal algorithm and the sliced orthogonal nonlinear generalized (SONG) correlation. The SONG correlation is based on the application of a linear correlation to corresponding binary slices of both the input scene and the reference object with appropriate weight factors. For a particular case, we show that the optimal strategy is a function of only the number of pixels for which the gray values in the noisy image match the ones of the reference image when the substitutive noise is uniformly distributed. This is exactly what a particular definition of the SONG correlation does.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2001-01-01 |