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IPM
30
YEARS OLD

“School of Cognitive Sciences”

Paper   IPM / Cognitive Sciences / 13435
   School of Cognitive Sciences
  Title: The role of higher-order statistical dependency of natural images in visual perception
  Author(s):
1 . H. MaBouDi
2 . M. Abouzari
  Status: In Proceedings
  Proceeding: Perception 41 ECVP Abstract Supplement, page 257
  Year: 2012
  Supported by: IPM
  Abstract:
In recent studies on computational vision from the efficient coding viewpoint, it assumed that visual sensory neurons are adjusted to the statistical properties of natural environment during sensory evolution. Thus understanding the statistics of natural images can help us to comprehend the function of visual sensory processing and perception. Natural images have non-random structures that reflect causal differences in the world. However, decorrelated natural images contain obvious structures but many of the important forms in the natural images require higher-order statistics to describe. It has been demonstrated that higher-order statistical structures of images are basis for the visual perception and object recognition and investigating such regularities could assist to clarify the spatiotemporal function of neurons in V1 and beyond. Nonetheless, higher-order statistics properties of natural scenes and their representation in the neural population are still unclear. In this study we extract spectra features from natural vs non-natural images by using statistical methods to construct an statistical model of natural image space. Our findings indicate that there are some significant differences between natural images and random spaces which are critical for visual perception. Our results will be useful for optimal modeling of visual system and can help to develop hierarchical models for learning non-linear regularities in natural images.

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