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IPM
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YEARS OLD

“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 14219
School of Cognitive Sciences
  Title:   Representation of higher-order statistical structures in natural scenes via spatial phase distributions
  Author(s): 
1.  H. MaBouDi
2.  H. Shimazaki
3.  S. Amari
4.  H. Soltanian-Zadeh
  Status:   Published
  Journal: Vision Research
  Vol.:  120
  Year:  2016
  Pages:   61-73
  Supported by:  IPM
  Abstract:
Natural scenes contain richer perceptual information in their spatial phase structure than their amplitudes. Modeling phase structure of natural scenes may explain higher-order structure inherent to the natural scenes, which is neglected in most classical models of redundancy reduction. Only recently, a few models have represented images using a complex form of receptive fields (RFs) and analyze their complex responses in terms of amplitude and phase. However, these complex representation models often tacitly assume a uniform phase distribution without empirical support. The structure of spatial phase distributions of natural scenes in the form of relative contributions of paired responses of RFs in quadrature has not been explored statistically until now. Here, we investigate the spatial phase structure of natural scenes using complex forms of various Gabor-like RFs. To analyze distributions of the spatial phase responses, we constructed a mixture model that accounts for multi-modal circular distributions, and the EM algorithm for estimation of the model parameters. Based on the likelihood, we report presence of both uniform and structured bimodal phase distributions in natural scenes. The latter bimodal distributions were symmetric with two peaks separated by about 180°. Thus, the redundancy in the natural scenes can be further removed by using the bimodal phase distributions obtained from these RFs in the complex representation models. These results predict that both phase invariant and phase sensitive complex cells are required to represent the regularities of natural scenes in visual systems.

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