Skip to main content
Article thumbnail
Location of Repository

c ○ 2003 Kluwer Academic Publishers. Manufactured in The Netherlands. On Advances in Statistical Modeling of Natural Images

By A. Srivastava, A. B. Lee and S. -c. Zhu

Abstract

Abstract. Statistical analysis of images reveals two interesting properties: (i) invariance of image statistics to scaling of images, and (ii) non-Gaussian behavior of image statistics, i.e. high kurtosis, heavy tails, and sharp central cusps. In this paper we review some recent results in statistical modeling of natural images that attempt to explain these patterns. Two categories of results are considered: (i) studies of probability models of images or image decompositions (such as Fourier or wavelet decompositions), and (ii) discoveries of underlying image manifolds while restricting to natural images. Applications of these models in areas such as texture analysis, image classification, compression, and denoising are also considered

Topics: natural image statistics, non-Gaussian models, scale invariance, statistical image analysis, image manifold, generalized Laplacian
Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.1469
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.cs.ualberta.ca/~btg... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.