scholarly journals Spatial structure, phase, and the contrast of natural images

2021 ◽  
Author(s):  
Reuben Rideaux ◽  
Rebecca K West ◽  
Peter J Bex ◽  
Jason B Mattingley ◽  
William J Harrison
Keyword(s):  
Spatial Structure ◽  
Natural Images ◽  
Natural Image ◽  
Local Contrast ◽  
Image Statistics ◽  
Visual Neuroscience ◽  
Environmental Statistics ◽  
Phase Alignment ◽  
Structure Phase ◽  
Local Background

The sensitivity of the human visual system is thought to be shaped by environmental statistics. A major endeavour in visual neuroscience, therefore, is to uncover the image statistics that predict perceptual and cognitive function. When searching for targets in natural images, for example, it has recently been proposed that target detection is inversely related to the spatial similarity of the target to its local background. We tested this hypothesis by measuring observers' sensitivity to targets that were blended with natural image backgrounds. Importantly, targets were designed to have a spatial structure that was either similar or dissimilar to the background. Contrary to masking from similarity, however, we found that observers were most sensitive to targets that were most similar to their backgrounds. We hypothesised that a coincidence of phase-alignment between target and background results in a local contrast signal that facilitates detection when target-background similarity is high. We confirmed this prediction in a second experiment. Indeed, we show that, by solely manipulating the phase of a target relative to its background, the target can be rendered easily visible or completely undetectable. Our study thus reveals a set of image statistics that predict how well people can perform the ubiquitous task of detecting an object in clutter.

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 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.

scholarly journals Foreground-background segmentation revealed during natural image viewing

2017 ◽  
Author(s):  
Paolo Papale ◽  
Andrea Leo ◽  
Luca Cecchetti ◽  
Giacomo Handjaras ◽  
Kendrick Kay ◽  
...  
Keyword(s):  
Natural Images ◽  
Natural Image ◽  
Automatic Process ◽  
Background Suppression ◽  
Visual Neuroscience ◽  
Passive Viewing ◽  
Show Evidence ◽  
Background Segmentation ◽  
Novel Method ◽  
Simplistic Model

AbstractOne of the major challenges in visual neuroscience is represented by foreground-background segmentation. Data from nonhuman primates show that segmentation leads to two distinct, but associated processes: the enhancement of neural activity during figure processing (i.e., foreground enhancement) and the suppression of background-related activity (i.e., background suppression). To study foreground-background segmentation in ecological conditions, we introduce a novel method based on parametric modulation of low-level image properties followed by application of simple computational image-processing models. By correlating the outcome of this procedure with human fMRI activity measured during passive viewing of 334 natural images, we reconstruct easily interpretable “neural images” from seven visual areas: V1, V2, V3, V3A, V3B, V4 and LOC. Results show evidence of foreground enhancement for all tested regions, while background suppression specifically occurs in V4 and LOC. “Neural images” reconstructed from V4 and LOC revealed a preserved spatial resolution of foreground textures, indicating a richer representation of the salient part of natural images, rather than a simplistic model of object shape. Our results indicate that scene segmentation is an automatic process that occurs during natural viewing, even when individuals are not required to perform any particular task.

scholarly journals Reconstruction of natural images from responses of primate retinal ganglion cells

2020 ◽  
Author(s):  
Nora Brackbill ◽  
Colleen Rhoades ◽  
Alexandra Kling ◽  
Nishal P. Shah ◽  
Alexander Sher ◽  
...  
Keyword(s):  
Receptive Field ◽  
Ganglion Cells ◽  
Natural Images ◽  
Natural Image ◽  
Retinal Ganglion ◽  
Natural Image Statistics ◽  
Image Statistics ◽  
Visual Message ◽  
Spatial Properties ◽  
Reconstruction Filter

AbstractThe visual message conveyed by a retinal ganglion cell (RGC) is often summarized by its spatial receptive field, but in principle also depends on the responses of other RGCs and natural image statistics. This possibility was explored by linear reconstruction of natural images from responses of the four numerically-dominant macaque RGC types. Reconstructions were highly consistent across retinas. The optimal reconstruction filter for each RGC – its visual message – reflected natural image statistics, and resembled the receptive field only when nearby, same-type cells were included. ON and OFF cells conveyed largely independent, complementary representations, and parasol and midget cells conveyed distinct and expected features. Correlated activity and nonlinearities had statistically significant but minor effects on reconstruction. Simulated reconstructions, using linear-nonlinear cascade models of RGC light responses that incorporated measured spatial properties and nonlinearities, produced similar results. Spatiotemporal reconstructions exhibited similar spatial properties, suggesting that the results are relevant for natural vision.

scholarly journals Neural Sensitivity to Natural Image Statistics Changes during Middle Childhood

2020 ◽  
Author(s):  
Benjamin Balas ◽  
Alyson Saville
Keyword(s):  
Middle Childhood ◽  
Visual Processing ◽  
Event Related Potentials ◽  
Developmental Trajectory ◽  
Natural Images ◽  
Natural Image ◽  
Natural Image Statistics ◽  
Image Perception ◽  
Image Statistics ◽  
Related Potentials

AbstractNatural images have lawful statistical properties that the adult visual system is sensitive to, both in terms of behavior and neural responses to natural images. The developmental trajectory of sensitivity to natural image statistics remains unclear, however. In behavioral tasks, children appear to slowly acquire adult-like sensitivity to natural image statistics during middle childhood (Ellemberg et al., 2012), but in other tasks, infants exhibit some sensitivity to deviations of natural image structure (Balas & Woods, 2014). Here, we used event-related potentials (ERPs) to examine how sensitivity to natural image statistics changes during childhood at distinct stages of visual processing (the P1 and N1 components). We asked children (5-10 years old) and adults to view natural texture images with either positive/negative contrast, and natural/synthetic texture appearance (Portilla & Simoncelli, 2000) to compare electrophysiological responses to images that did or did not violate natural statistics. We hypothesized that children may only acquire sensitivity to these deviations from natural texture appearance late in middle childhood. Counter to this hypothesis, we observed significant responses to unnatural contrast and texture statistics at the N1 in all age groups. At the P1, however, only young children exhibited sensitivity to contrast polarity. The latter effect suggests greater sensitivity earlier in development to some violations of natural image statistics. We discuss these results in terms of changing patterns of invariant texture processing during middle childhood and ongoing refinement of the representations supporting natural image perception.

scholarly journals Spatial structure, phase, and the contrast of natural images

2022 ◽  
Vol 22 (1) ◽  
pp. 4
Author(s):  
Reuben Rideaux ◽  
Rebecca K. West ◽  
Thomas S. A. Wallis ◽  
Peter J. Bex ◽  
Jason B. Mattingley ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Natural Images ◽  
Structure Phase

scholarly journals Two-Dimensional Hermite Filters Simplify the Description of High-Order Statistics of Natural Images

2016 ◽  
Author(s):  
Qin Hu ◽  
Jonathan Victor
Keyword(s):  
Order Statistics ◽  
Dimensional Subspace ◽  
High Order ◽  
Natural Images ◽  
Natural Image ◽  
Two Dimensional ◽  
Natural Image Statistics ◽  
Image Statistics ◽  
High Order Statistics ◽  
Orthogonal Set

AbstractNatural image statistics play a crucial role in shaping biological visual systems, understanding their function and design principles, and designing effective computer-vision algorithms. High-order statistics are critical for conveying local features, but they are challenging to study – largely because their number and variety is large. Here, via the use of two-dimensional Hermite (TDH) functions, we identify a covert symmetry in high-order statistics of natural images that simplifies this task. This emerges from the structure of TDH functions, which are an orthogonal set of functions that are organized into a hierarchy of ranks. Specifically, we find that the shape (skewness and kurtosis) of the distribution of filter coefficients depends only on the projection of the function onto a 1-dimensional subspace specific to each rank. The characterization of natural image statistics provided by TDH filter coefficients reflects both their phase and amplitude structure, and we suggest an intuitive interpretation for the special subspace within each rank.

scholarly journals Redundancy between spectral and higher-order texture statistics for natural image segmentation

2021 ◽  
Author(s):  
Daniel Herrera-Esposito ◽  
Leonel Gomez-Sena ◽  
Ruben Coen-Cagli
Keyword(s):  
Image Segmentation ◽  
Peripheral Vision ◽  
Higher Order ◽  
Natural Images ◽  
Natural Image ◽  
Natural Image Statistics ◽  
Image Statistics ◽  
Texture Model ◽  
Spectral Statistics ◽  
Perceptual Segmentation

Visual texture, defined by local image statistics, provides important information to the human visual system for perceptual segmentation. Second-order or spectral statistics (equivalent to the Fourier power spectrum) are a well-studied segmentation cue. However, the role of higher-order statistics (HOS) in segmentation remains unclear, particularly for natural images. Recent experiments indicate that, in peripheral vision, the HOS of the widely adopted Portilla-Simoncelli texture model are a weak segmentation cue compared to spectral statistics, despite the fact that both are necessary to explain other perceptual phenomena and to support high-quality texture synthesis. Here we test whether this discrepancy reflects a property of natural image statistics. First, we observe that differences in spectral statistics across segments of natural images are redundant with differences in HOS. Second, using linear and nonlinear classifiers, we show that each set of statistics individually affords high performance in natural scenes and texture segmentation tasks, but combining spectral statistics and HOS produces relatively small improvements. Third, we find that HOS improve segmentation for a subset of images, although these images are difficult to identify. We also find that different subsets of HOS improve segmentation to a different extent, in agreement with previous physiological and perceptual work. These results show that the HOS add modestly to spectral statistics for natural image segmentation. We speculate that tuning to natural image statistics under resource constraints could explain the weak contribution of HOS to perceptual segmentation in human peripheral vision.

scholarly journals Reconstruction of natural images from responses of primate retinal ganglion cells

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Nora Brackbill ◽  
Colleen Rhoades ◽  
Alexandra Kling ◽  
Nishal P Shah ◽  
Alexander Sher ◽  
...  
Keyword(s):  
Receptive Field ◽  
Ganglion Cells ◽  
Natural Images ◽  
Natural Image ◽  
Retinal Ganglion ◽  
Natural Image Statistics ◽  
Image Statistics ◽  
Visual Message ◽  
Spatial Properties ◽  
Reconstruction Filter

The visual message conveyed by a retinal ganglion cell (RGC) is often summarized by its spatial receptive field, but in principle also depends on the responses of other RGCs and natural image statistics. This possibility was explored by linear reconstruction of natural images from responses of the four numerically-dominant macaque RGC types. Reconstructions were highly consistent across retinas. The optimal reconstruction filter for each RGC – its visual message – reflected natural image statistics, and resembled the receptive field only when nearby, same-type cells were included. ON and OFF cells conveyed largely independent, complementary representations, and parasol and midget cells conveyed distinct features. Correlated activity and nonlinearities had statistically significant but minor effects on reconstruction. Simulated reconstructions, using linear-nonlinear cascade models of RGC light responses that incorporated measured spatial properties and nonlinearities, produced similar results. Spatiotemporal reconstructions exhibited similar spatial properties, suggesting that the results are relevant for natural vision.

Local Contrast in Natural Images: Normalisation and Coding Efficiency

Perception ◽  
10.1068/p2996 ◽  
2000 ◽  
Vol 29 (9) ◽  
pp. 1041-1055 ◽  
Author(s):  
Nuala Brady ◽  
David J Field
Keyword(s):  
Natural Images ◽  
Local Contrast ◽  
Coding Efficiency
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