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.

Natural-Image Discrimination in the Periphery: The Importance of Phase and Amplitude Information

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 85-85
Author(s):  
G M Kennedy ◽  
D J Tolhurst
Keyword(s):  
Phase Change ◽  
Scaling Factor ◽  
Natural Images ◽  
Natural Image ◽  
Rapid Decline ◽  
Natural Scenes ◽  
Phase Identification ◽  
Phase Information ◽  
Black And White ◽  
Sinusoidal Gratings

Previous studies with simplified stimuli such as combinations of sinusoidal gratings have revealed phase identification losses in the periphery that are not eliminated by a scaling factor. How do these phase processing problems influence our ability to discriminate natural images in the periphery? In this study the ability of an observer to identify the ‘odd-image-out’ when there is either an amplitude-only, phase-only, or amplitude and phase change in one out of three stimuli is compared. Pairs of Fourier-manipulated black-and-white digitised photographs of natural images were used and phase and amplitude spectral exchanges of varying proportions were made between two different images. Measurements were made to determine the smallest phase change needed in order for the observer to reliably discriminate the manipulated image, compared to two reference stimuli, at eccentricities of 0°, 2.5°, 5°, and 10°. This was compared to discrimination thresholds found when amplitude and phase, and amplitude alone were exchanged. The ability to discriminate images on the basis of phase information alone did fall off quickly with eccentricity (comparable to phase and amplitude discriminations). However, there was a much more rapid decline in amplitude-only discrimination. It appears that phase information in natural scenes remains a relatively more important visual cue in the periphery than amplitude.

scholarly journals The human visual system preserves the hierarchy of 2-dimensional pattern regularity

2021 ◽  
Author(s):  
Peter J. Kohler ◽  
Alasdair D. F. Clarke
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Human Visual System ◽  
Posterior Probability ◽  
Brain Activity ◽  
Large Body ◽  
Natural Scenes ◽  
Brain Response ◽  
Lower Amplitude ◽  
Wallpaper Groups

AbstractSymmetries are present at many scales in images of natural scenes. A large body of literature has demonstrated contributions of symmetry to numerous domains of visual perception. The four fundamental symmetries, reflection, rotation, translation and glide reflection, can be combined in exactly 17 distinct ways. These wallpaper groups represent the complete set of symmetries in 2D images and have recently found use in the vision science community as an ideal stimulus set for studying the perception of symmetries in textures. The goal of the current study is to provide a more comprehensive description of responses to symmetry in the human visual system, by collecting both brain imaging (Steady-State Visual Evoked Potentials measured using high-density EEG) and behavioral (symmetry detection thresholds) data using the entire set of wallpaper groups. This allows us to probe the hierarchy of complexity among wallpaper groups, in which simpler groups are subgroups of more complex ones. We find that this hierarchy is preserved almost perfectly in both behavior and brain activity: A multi-level Bayesian GLM indicates that for most of the 63 subgroup relationships, subgroups produce lower amplitude responses in visual cortex (posterior probability: > 0.95 for 56 of 63) and require longer presentation durations to be reliably detected (posterior probability: > 0.95 for 49 of 63). This systematic pattern is seen only in visual cortex and only in components of the brain response known to be symmetric-specific. Our results show that representations of symmetries in the human brain are precise and rich in detail, and that this precision is reflected in behavior. These findings expand our understanding of symmetry perception, and open up new avenues for research on how fine-grained representations of regular textures contribute to natural vision.

scholarly journals Coding of navigational affordances in the human visual system

2017 ◽  
Vol 114 (18) ◽  
pp. 4793-4798 ◽  
Author(s):  
Michael F. Bonner ◽  
Russell A. Epstein
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Spatial Navigation ◽  
The Other ◽  
Visual Features ◽  
Central Component ◽  
Natural Scenes ◽  
Unknown Mechanism ◽  
Population Codes

A central component of spatial navigation is determining where one can and cannot go in the immediate environment. We used fMRI to test the hypothesis that the human visual system solves this problem by automatically identifying the navigational affordances of the local scene. Multivoxel pattern analyses showed that a scene-selective region of dorsal occipitoparietal cortex, known as the occipital place area, represents pathways for movement in scenes in a manner that is tolerant to variability in other visual features. These effects were found in two experiments: One using tightly controlled artificial environments as stimuli, the other using a diverse set of complex, natural scenes. A reconstruction analysis demonstrated that the population codes of the occipital place area could be used to predict the affordances of novel scenes. Taken together, these results reveal a previously unknown mechanism for perceiving the affordance structure of navigable space.

scholarly journals Spatiochromatic Receptive Field Properties Derived from Information-Theoretic Analyses of Cone Mosaic Responses to Natural Scenes

2003 ◽  
Vol 15 (2) ◽  
pp. 397-417 ◽  
Author(s):  
Eizaburo Doi ◽  
Toshio Inui ◽  
Te-Won Lee ◽  
Thomas Wachtler ◽  
Terrence J. Sejnowski
Keyword(s):  
Visual System ◽  
Receptive Fields ◽  
Independent Component ◽  
Natural Images ◽  
Natural Scenes ◽  
Subsequent Stage ◽  
Type I ◽  
Sensory Signals ◽  
Cone Mosaic ◽  
Unsupervised Neural Network

Neurons in the early stages of processing in the primate visual system efficiently encode natural scenes. In previous studies of the chromatic properties of natural images, the inputs were sampled on a regular array, with complete color information at every location. However, in the retina cone photoreceptors with different spectral sensitivities are arranged in a mosaic. We used an unsupervised neural network model to analyze the statistical structure of retinal cone mosaic responses to calibrated color natural images. The second-order statistical dependencies derived from the covariance matrix of the sensory signals were removed in the first stage of processing. These decorrelating filters were similar to type I receptive fields in parvo- or konio-cellular LGN in both spatial and chromatic characteristics. In the subsequent stage, the decorrelated signals were linearly transformed to make the output as statistically independent as possible, using independent component analysis. The independent component filters showed luminance selectivity with simple-cell-like receptive fields, or had strong color selectivity with large, often double-opponent, receptive fields, both of which were found in the primary visual cortex (V1). These results show that the “form” and “color” channels of the early visual system can be derived from the statistics of sensory signals.

Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds

2009 ◽  
Vol 26 (1) ◽  
pp. 93-108 ◽  
Author(s):  
SHENG ZHANG ◽  
CRAIG K. ABBEY ◽  
MIGUEL P. ECKSTEIN
Keyword(s):  
Receptive Field ◽  
White Noise ◽  
Receptive Fields ◽  
Image Data ◽  
Optimization Method ◽  
Natural Images ◽  
Natural Image ◽  
Natural Scenes ◽  
Linear Combinations ◽  
Data Set

AbstractThe neural mechanisms driving perception and saccades during search use information about the target but are also based on an inhibitory surround not present in the target luminance profile (e.g., Eckstein et al., 2007). Here, we ask whether these inhibitory surrounds might reflect a strategy that the brain has adapted to optimize the search for targets in natural scenes. To test this hypothesis, we sought to estimate the best linear template (behavioral receptive field), built from linear combinations of Gabor channels representing V1 simple cells in search for an additive Gaussian target embedded in natural images. Statistically nonstationary and non-Gaussian properties of natural scenes preclude calculation of the best linear template from analytic expressions and require an iterative optimization method such as a virtual evolution via a genetic algorithm. Evolved linear receptive fields built from linear combinations of Gabor functions include substantial inhibitory surround, larger than those found in humans performing target search in white noise. The inhibitory surrounds were robust to changes in the contrast of the signal, generalized to a larger calibrated natural image data set, and tasks in which the signal occluded other objects in the image. We show that channel nonlinearities can have strong effects on the observed linear behavioral receptive field but preserve the inhibitory surrounds. Together, the results suggest that the apparent suboptimality of inhibitory surrounds in human behavioral receptive fields when searching for a target in white noise might reflect a strategy to optimize detection of signals in natural scenes. Finally, we contend that optimized linear detection of spatially compact signals in natural images might be a new possible hypothesis, distinct from decorrelation of visual input and sparse representations (e.g., Graham et al., 2006), to explain the evolution of center–surround organization of receptive fields in early vision.

scholarly journals Natural image statistics at depth edges modulate perceptual stability

2020 ◽  
Author(s):  
Zeynep Başgöze ◽  
David N. White ◽  
Johannes Burge ◽  
Emily A. Cooper
Keyword(s):  
Visual System ◽  
Three Dimensional ◽  
Natural Image ◽  
Natural Scenes ◽  
Natural Environments ◽  
Large Set ◽  
Natural Image Statistics ◽  
Binocular Fusion ◽  
Perceptual Stability ◽  
Statistical Regularities

AbstractBinocular fusion relies on matching points in the two eyes that correspond to the same physical feature in the world. However, not all world features are binocularly visible. In particular, at depth edges parts of a scene are often visible to only one eye (so-called half occlusions). Accurate detection of these monocularly visible regions is likely to be important for stable visual perception. If monocular regions are not detected as such, the visual system may attempt to binocularly fuse non-corresponding points, which can result in unstable percepts. We investigated the hypothesis that the visual system capitalizes upon statistical regularities associated with depth edges in natural scenes to aid binocular fusion and facilitate perceptual stability. By sampling from a large set of stereoscopic natural image patches, we found evidence that monocularly visible regions near depth edges in natural scenes tend to have features more visually similar to the adjacent binocularly visible background region than to the adjacent binocularly visible foreground. The generality of these results was supported by a parametric study of three-dimensional (3D) viewing geometry in simulated environments. In two perceptual experiments, we examined if this statistical regularity may be leveraged by the visual system. The results show that perception tended to be more stable when the visual properties of the depth edge were statistically more likely. Exploiting regularities in natural environments may allow the visual system to facilitate fusion and perceptual stability of natural scenes when both binocular and monocular regions are visible.PrecisWe report an analysis of natural scenes and two perceptual studies aimed at understanding how the visual statistics of depth edges impact perceptual stability. Our results suggest that the visual system exploits natural scene regularities to aid binocular fusion and facilitate perceptual stability.

scholarly journals Object Recognition Inspiring HVS

2018 ◽  
Vol 12 (2) ◽  
pp. 783
Author(s):  
Mohammadesmaeil Akbarpour ◽  
Nasser Mehrshad ◽  
Seyyed-Mohammad Razavi
Keyword(s):  
Object Recognition ◽  
Visual System ◽  
Computational Modeling ◽  
Hierarchical Model ◽  
Human Visual System ◽  
Visual Pathway ◽  
Natural Images ◽  
Significant Performance

<p><span>Human recognize objects in complex natural images very fast within a fraction of a second. Many computational object recognition models inspired from this powerful ability of human. The Human Visual System (HVS) recognizes object in several processing layers which we know them as hierarchically model. Due to amazing complexity of HVS and the connections in visual pathway, computational modeling of HVS directly from its physiology is not possible. So it considered as a some blocks and each block modeled separately. One models inspiring of HVS is HMAX which its main problem is selecting patches in random way. As HMAX is a hierarchical model, HMAX can enhanced with enhancing each layer separately. In this paper instead of random patch extraction, Desirable Patches for HMAX (DPHMAX) will extracted.  HVS for extracting patch first selected patches with more information. For simulating this block patches with more variance will be selected. Then HVS will chose patches with more similarity in a class. For simulating this block one algorithm is used. For evaluating proposed method, Caltech 5 and Caltech101 datasets are used. Results show that the proposed method (DPMAX) provides a significant performance over HMAX and other models with the same framework.</span></p>

scholarly journals Natural Image Statistics for Mouse Vision

2021 ◽  
Author(s):  
Luca Abballe ◽  
Hiroki Asari
Keyword(s):  
Visual Field ◽  
Uv Light ◽  
Green Light ◽  
Mouse Retina ◽  
Natural Image ◽  
Natural Scenes ◽  
Lower Visual Field ◽  
Local Contrast ◽  
Natural Image Statistics ◽  
Image Statistics

The mouse has dichromatic colour vision based on two different types of opsins: short (S)-and middle (M)-wavelength-sensitive opsins with peak sensitivity to ultraviolet (UV; 360 nm) and green light (508 nm), respectively. In the mouse retina, the cone photoreceptors that predominantly express the S-opsin are more sensitive to contrasts, and denser towards the ventral retina, preferentially sampling the upper part of the visual field. In contrast, the expression of the M-opsin gradually increases towards the dorsal retina that encodes the lower visual field. Such distinct retinal organizations are assumed to arise from a selective pressure in evolution to efficiently encode the natural scenes. However, natural image statistics of UV light have never been examined beyond the spectral analysis. Here we developed a multi-spectral camera and examined the UV and green image statistics of the same natural scenes. We found that the local contrast and the spatial correlation were higher in UV than in green for images above the horizon, but lower in UV than in green for those below the horizon. This suggests that the mouse retina is not necessarily optimal for maximizing the bandwidth of information transmission. Factors besides the coding efficiency, such as visual behavioural requirements, will thus need to be considered to fully explain the characteristic organization of the mouse retina.

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