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Dead leaves and the dirty ground: low-level image statistics in transmissive and occlusive imaging environments
- Publication Year :
- 2012
-
Abstract
- The opacity of typical objects in the world results in occlusion --- an important property of natural scenes that makes inference of the full 3-dimensional structure of the world challenging. The relationship between occlusion and low-level image statistics has been hotly debated in the literature, and extensive simulations have been used to determine whether occlusion is responsible for the ubiquitously observed power-law power spectra of natural images. To deepen our understanding of this problem, we have analytically computed the 2- and 4-point functions of a generalized "dead leaves" model of natural images with parameterized object transparency. Surprisingly, transparency alters these functions only by a multiplicative constant, so long as object diameters follow a power law distribution. For other object size distributions, transparency more substantially affects the low-level image statistics. We propose that the universality of power law power spectra for both natural scenes and radiological medical images -- formed by the transmission of x-rays through partially transparent tissue -- stems from power law object size distributions, independent of object opacity.<br />20 pages, 4 figures. Matches the version accepted to Phys Rev E
- Subjects :
- Diagnostic Imaging
Models, Statistical
Fourier Analysis
Statistical Mechanics (cond-mat.stat-mech)
Opacity
Computer science
X-Rays
Occlusive
Biophysics
FOS: Physical sciences
Parameterized complexity
Inference
Models, Theoretical
Universality (dynamical systems)
FOS: Biological sciences
Quantitative Biology - Neurons and Cognition
Statistics
Humans
Radiographic Image Interpretation, Computer-Assisted
Neurons and Cognition (q-bio.NC)
Multiplicative constant
Algorithms
Vision, Ocular
Condensed Matter - Statistical Mechanics
Probability
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....509e656ab5afa903390ac0047c03ca64