An essential feature of a practical automatic image recognition system is the ability to tolerate certain types of variations within images. The recognition of images subject to intrinsic variations can be treated as a sorting task in which an image is identified as a member of some class of images. Herein, the maximum-likelihood strategy, an important tool in the field of statistical decision theory, is applied to the image classification problem. We show that the strategy can be implemented in a standard image correlation system and that excellent classification results can be obtained.
Maximum-Likelihood Image Classification
Digital and Optical Shape Representation and Pattern Recognition ; 1988 ; Orlando,FL,United States
Proc. SPIE ; 0938
1988-08-22
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Maximum likelihood autocalibration
British Library Online Contents | 2011
|A Maximum Likelihood Approach to Texture Classification using Wavelet Transform
British Library Conference Proceedings | 1994
|Massively parallel spatially variant maximum likelihood image restoration
British Library Conference Proceedings | 1995
|Maximum-likelihood data decoder
NTRS | 1979
|