scholarly journals Local image statistics: A highly conserved perceptual space encompassing statistics of low and high order

2013 ◽  
Vol 13 (9) ◽  
pp. 1233-1233
Author(s):  
J. Victor ◽  
D. Thengone ◽  
C. Chubb ◽  
M. Conte
Keyword(s):  
High Order ◽  
Perceptual Space ◽  
Image Statistics ◽  
Local Image

scholarly journals Approximately uniform isodiscrimination contours within a perceptual space of local image statistics

2015 ◽  
Vol 15 (12) ◽  
pp. 774
Author(s):  
Mary Conte ◽  
Syed Rizvi ◽  
Jonathan Victor
Keyword(s):  
Perceptual Space ◽  
Image Statistics ◽  
Local Image

scholarly journals A perceptual space of local image statistics

2015 ◽  
Vol 117 ◽  
pp. 117-135 ◽  
Author(s):  
Jonathan D. Victor ◽  
Daniel J. Thengone ◽  
Syed M. Rizvi ◽  
Mary M. Conte
Keyword(s):  
Perceptual Space ◽  
Image Statistics ◽  
Local Image

scholarly journals Border salience reveals a curved global geometry of the perceptual space of local image statistics

2014 ◽  
Vol 14 (10) ◽  
pp. 654-654
Author(s):  
S. M. Rizvi ◽  
M. M. Conte ◽  
J. D. Victor
Keyword(s):  
Perceptual Space ◽  
Image Statistics ◽  
Global Geometry ◽  
Local Image

scholarly journals The role of local image statistics in separating figure from ground

2019 ◽  
Vol 19 (10) ◽  
pp. 124a
Author(s):  
Jonathan Victor ◽  
Mary M Conte
Keyword(s):  
Image Statistics ◽  
Local Image

scholarly journals Analyzing local image statistics via maximum-entropy constructions

2011 ◽  
Author(s):  
Jonathan Victor ◽  
Daniel Thengone ◽  
Mary Conte
Keyword(s):  
Maximum Entropy ◽  
Image Statistics ◽  
Local Image

scholarly journals Systematic differences between visually-relevant global and local image statistics of brain MRI and natural scenes

2018 ◽  
Author(s):  
Yueyang Xu ◽  
Ashish Raj ◽  
Jonathan Victor ◽  
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Image Formation ◽  
Natural Images ◽  
Natural Image ◽  
Natural Scenes ◽  
Image Statistics ◽  
Processing Technologies ◽  
Global And Local ◽  
Local Image

AbstractAn important heuristic in developing image processing technologies is to mimic the computational strategies used by humans. Relevant to this, recent studies have shown that the human brain’s processing strategy is closely matched to the characteristics of natural scenes, both in terms of global and local image statistics. However, structural MRI images and natural scenes have fundamental differences: the former are two-dimensional sections through a volume, the latter are projections. MRI image formation is also radically different from natural image formation, involving acquisition in Fourier space, followed by several filtering and processing steps that all have the potential to alter image statistics. As a consequence, aspects of the human visual system that are finely-tuned to processing natural scenes may not be equally well-suited for MRI images, and identification of the differences between MRI images and natural scenes may lead to improved machine analysis of MRI.With these considerations in mind, we analyzed spectra and local image statistics of MRI images in several databases including T1 and FLAIR sequence types and of simulated MRI images,[1]–[6] and compared this analysis to a parallel analysis of natural images[7] and visual sensitivity[7][8]. We found substantial differences between the statistical features of MRI images and natural images. Power spectra of MRI images had a steeper slope than that of natural images, indicating a lack of scale invariance. Independent of this, local image statistics of MRI and natural images differed: compared to natural images, MRI images had smaller variations in their local two-point statistics and larger variations in their local three-point statistics – to which the human visual system is relatively insensitive. Our findings were consistent across MRI databases and simulated MRI images, suggesting that they result from brain geometry at the scale of MRI resolution, rather than characteristics of specific imaging and reconstruction methods.

scholarly journals Local image statistics have a perceptual metric that is nearly Euclidean

2012 ◽  
Vol 12 (9) ◽  
pp. 97-97
Author(s):  
J. Victor ◽  
D. Thengone ◽  
M. Conte
Keyword(s):  
Image Statistics ◽  
Local Image
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