Detection of changes in the amplitude spectra of natural images is explained by a band-limited local-contrast model

1996 ◽  
Author(s):  
David J. Tolhurst ◽  
Yoav Tadmor ◽  
G. Arthurs
Keyword(s):  
Natural Images ◽  
Local Contrast ◽  
Contrast Model ◽  
Amplitude Spectra ◽  
Band Limited

Discrimination of Changes in the Slopes of the Amplitude Spectra of Natural Images: Band-Limited Contrast and Psychometric Functions

Perception ◽  
1997 ◽  
Vol 26 (8) ◽  
pp. 1011-1025 ◽  
Author(s):  
David J Tolhurst ◽  
Yoav Tadmor
Keyword(s):  
Spatial Frequency ◽  
Discrimination Task ◽  
Spatial Scales ◽  
Natural Images ◽  
Natural Scenes ◽  
Local Contrast ◽  
Frequency Bands ◽  
Amplitude Spectra ◽  
Lower Frequency Band ◽  
Band Limited

Thresholds were measured for discriminating changes in the slopes of the amplitude spectra of stimuli derived from photographs of natural scenes and from random-luminance patterns. The variety and magnitudes of the thresholds could be explained by a model based on the discrimination of the changes in band-limited local contrast. Different spatial scales of local contrast (or different spatial-frequency bands of about 1 octave) were implicated for different reference spectral slopes; the model implicated a lower frequency-band for stimuli with shallower amplitude spectra. The implications of the model were tested experimentally by using stimuli in which the spectra were changed within restricted spatial-frequency bands. When the amplitude spectra of the test and reference stimuli differed only within the implicated frequency bands, thresholds were affected little. However, when the test and reference spectra differed at all frequencies except those in the implicated bands, the thresholds were elevated markedly. The forms of the psychometric functions for the discrimination task were entirely compatible with the hypothesis that the task relies upon the ability to discriminate changes of contrast. The Weibull functions fitted to the data had slope parameters (β) in the range 1 to 3, compatible with discrimination of low (but suprathreshold) contrasts.

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

scholarly journals Human estimation of local contrast orientation in natural images

2010 ◽  
Vol 9 (8) ◽  
pp. 1048-1048
Author(s):  
G. Hoff ◽  
M. Brady
Keyword(s):  
Natural Images ◽  
Local Contrast ◽  
Human Estimation

The Minimal Local-Asperity Hypothesis of Early Retinal Lateral Inhibition

2000 ◽  
Vol 12 (7) ◽  
pp. 1485-1517 ◽  
Author(s):  
Rosario M. Balboa ◽  
Norberto M. Grzywacz
Keyword(s):  
Information Theory ◽  
Lateral Inhibition ◽  
Horizontal Cells ◽  
Natural Images ◽  
Local Contrast ◽  
Spatial Profile ◽  
Probabilistic Knowledge ◽  
Attribute Extraction ◽  
Extraction Error ◽  
Do So

Recently we found that the theories related to information theory existent in the literature cannot explain the behavior of the extent of the lateral inhibition mediated by retinal horizontal cells as a function of background light intensity. These theories can explain the fall of the extent from intermediate to high intensities, but not its rise from dim to intermediate intensities. We propose an alternate hypothesis that accounts for the extent's bell-shape behavior. This hypothesis proposes that the lateral-inhibition adaptation in the early retina is part of a system to extract several image attributes, such as occlusion borders and contrast. To do so, this system would use prior probabilistic knowledge about the biological processing and relevant statistics in natural images. A key novel statistic used here is the probability of the presence of an occlusion border as a function of local contrast. Using this probabilistic knowledge, the retina would optimize the spatial profile of lateral inhibition to minimize attribute-extraction error. The two significant errors that this minimization process must reduce are due to the quantal noise in photoreceptors and the straddling of occlusion borders by lateral inhibition.

Contrast Adaptation and the Spatial Structure of Natural Images

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 174-174
Author(s):  
M A Webster ◽  
O H MacLin ◽  
A L Rees ◽  
V E Raker
Keyword(s):  
Selective Adaptation ◽  
Natural Images ◽  
Spatial Frequencies ◽  
Outdoor Scenes ◽  
Amplitude Spectra ◽  
Contrast Pattern ◽  
Biased Perception ◽  
Face Adaptation ◽  
Contrast Thresholds ◽  
Human Faces

Contrast (pattern-selective) adaptation influences perception by adjusting sensitivity to the prevailing pattern of stimulation. We asked how the state of adaptation might depend on the patterns of spatial contrast typical of the natural visual environment. In one set of experiments, we examined whether adaptation to the characteristic amplitude spectra of natural images (which tend to decrease with frequency as 1/f) induces characteristic changes in contrast sensitivity. Contrast thresholds and suprathreshold contrast matches were measured after adaptation to random samples from an ensemble of images of natural outdoor scenes, or synthetic images formed by filtering the amplitude spectra of noise over a range of slopes. Adaptation differentially reduced sensitivity at low to medium spatial frequencies, but losses were not strongly dependent on the slope of the adapting spectra. In a second set of experiments, we examined the figural aftereffects induced by adaptation to naturalistic stimuli, by adapting and testing with images of human faces, for which small configural changes are highly discriminable. Observers adapted to frontal-view images of faces that were distorted by local expansions or contractions about the centre, and then adjusted distortions in test images to try to select the original face. Adaptation strongly biased perception in a direction opposite to the adapting distortion, with strongest aftereffects when test and adapting stimuli were derived from the same face image. Our results suggest that adaptation to the stimuli encountered in the course of normal viewing may exert ubiquitous and selective influences that are important in characterising the normal operating state of the visual system.

scholarly journals The statistics of local contrast and mean luminance in natural images

2004 ◽  
Vol 4 (8) ◽  
pp. 48-48
Author(s):  
R. A. Frazor ◽  
W. S. Geisler
Keyword(s):  
Natural Images ◽  
Local Contrast ◽  
Mean Luminance

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.

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