Statistical Principles in Image Modeling

Statistical Principles in Image Modeling

Images of natural scenes contain a rich variety of visual patterns. To learn and recognize these patterns from natural images, it is necessary to construct statistical models for these patterns. In this review article we describe three statistical principles for modeling image patterns: the sparse coding principle, the minimax entropy principle, and the meaningful alignment principle. We explai...

Fieguth Statistical Image Processing and Multidimensional Modeling

1 Introduction Images are all around us! Inexpensive digital cameras, video cameras, computer web-cams, satellite imagery, and images off the Internet give us access to spatial imagery of all sorts. The vast majority of these images will be of scenes at human scales — pictures of animals / houses / people / faces and so on — relatively complex images which are not well described statistically o...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models

Principles of Statistical Inference

Statistical theory aims to provide a foundation for studying the collection and interpretation of data that does not depend on the particular details of the substantive field in which the data is being considered. This gives a systematic way to approach new problems, and a common language for summarizing results; ideally the foundations and common language ensure that statistical aspects of one...

Principles of statistical astrometry

We present a coherent scheme of ‘statistical astrometry’ for high-precision measurements. Statistical astrometry provides methods and tools for treating quantitatively the overall statistical effect of the presence of many individually unresolved or unmodelled astrometric binaries in a stellar ensemble. The non-linear motions of the photo-centers of these binaries give rise to deviations from l...