Title :
Discrete space models for self-similar random images
Author :
Lee, Seungsin ; Rao, Raghuveer
Author_Institution :
Center for Imaging Sci., Rochester Inst. of Technol., NY, USA
Abstract :
Images exhibiting statistical self-similarity are of interest in various areas of image processing such as textures and scene synthesis. In continuous-space, statistical self-similarity is defined through statistics invariant to spatial scaling. However, because of lack of discrete-space scaling operation, statistical self-similarity in discrete-space has been characterized by approaches such as increments of fractional Brownian motion rather than scaling. We address these two issues regarding self-similar random fields through the paper. We show that the current self-similarity definition for continuous-space is somewhat restrictive, and we offer a new self-similarity definition in continuous-space more general than the current one. Furthermore, we provide a new formalism for statistical self-similarity in discrete-space by defining a scaling operation for discrete-space images. Consequently, a wider class of self-similar random images can be synthesized for different applications in discrete-space. The paper presents theoretical development and synthesis examples.
Keywords :
fractals; image texture; matrix algebra; statistical analysis; discrete space models; image processing; image textures; scaling operation; scene synthesis; self-similar random fields; self-similar random images; statistical self-similarity; Biomedical imaging; Brownian motion; Digital images; Image processing; Image segmentation; Layout; Mathematical model; Remote sensing; Space technology; Statistics;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
Print_ISBN :
0-7803-8484-9
DOI :
10.1109/ICASSP.2004.1326536