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Self-Similar Random Field Models in Discrete Space.
- Source :
- IEEE Transactions on Image Processing; Jan2006, Vol. 15 Issue 1, p160-168, 9p
- Publication Year :
- 2006
-
Abstract
- Self-similar random fields are of interest in various areas of image processing since they fit certain types of natural pat- terns and textures. Current treatments of self-similarity in continuous two-dimensional (2-D) space use a definition that is a direct extension of the one-dimensional definition, which requires invariance of the statistics of a random process to time scaling. Current discrete-space 2-D approaches do not consider scaling, but, instead, are based on ad hoc formulations, such as digitizing continuous random fields. In this paper, we show that the current statistical self-similarity definition in continuous space is restrictive and provide an alternative, more general definition. We also provide a formalism for discrete-space statistical self-similarity that relies on a new scaling operator for discrete images. Within the new framework, it is possible to synthesize a wider class of discrete-space self-similar random fields and texture images. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 15
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 19369580
- Full Text :
- https://doi.org/10.1109/TIP.2005.860331