Chromatic spatial frequency response of the human vision-chromatic axes with spatial frequency bandwidth at colored point

1981 ◽  
Vol 64 (2) ◽  
pp. 11-21
Author(s):  
Haruo Sakata
Keyword(s):  
Spatial Frequency ◽  
Frequency Response ◽  
Human Vision ◽  
Frequency Bandwidth ◽  
Colored Point

Adapted and unadapted spatial frequency channels in human vision

1973 ◽  
Vol 13 (11) ◽  
pp. 2139-2143 ◽  
Author(s):  
Robert V. Lange ◽  
Claude Sige ◽  
Sidney Stecher
Keyword(s):  
Spatial Frequency ◽  
Human Vision

The Study of Spatial Frequency Channels for Human Visual System

2019 ◽  
Vol 33 (06) ◽  
pp. 1955007
Author(s):  
Xiangyang Xu ◽  
Qiao Chen ◽  
Ruixin Xu
Keyword(s):  
Receptive Field ◽  
Spatial Frequency ◽  
Visual System ◽  
Human Visual System ◽  
Frequency Tuning ◽  
Tuning Curve ◽  
Vision System ◽  
Human Vision ◽  
Psychophysical Experiment ◽  
Human Vision System

Similar to auditory perception of sound system, color perception of the human visual system also presents a multi-frequency channel property. In order to study the multi-frequency channel mechanism of how the human visual system processes color information, the paper proposed a psychophysical experiment to measure the contrast sensitivities based on 17 color samples of 16 spatial frequencies on CIELAB opponent color space. Correlation analysis was carried out on the psychophysical experiment data, and the results show obvious linear correlations of observations for different spatial frequencies of different observers, which indicates that a linear model can be used to model how human visual system processes spatial frequency information. The results of solving the model based on the experiment data of color samples show that 9 spatial frequency tuning curves can exist in human visual system with each lightness, R–G and Y–B color channel and each channel can be represented by 3 tuning curves, which reflect the “center-around” form of the human visual receptive field. It is concluded that there are 9 spatial frequency channels in human vision system. The low frequency tuning curve of a narrow-frequency bandwidth shows the characteristics of lower level receptive field for human vision system, the medium frequency tuning curve shows a low pass property of the change of medium frequent colors and the high frequency tuning curve of a width-frequency bandwidth, which has a feedback effect on the low and medium frequency channels and shows the characteristics of higher level receptive field for human vision system, which represents the discrimination of details.

Orientation Specificity and Spatial Selectivity in Human Vision

Perception ◽  
1973 ◽  
Vol 2 (1) ◽  
pp. 53-60 ◽  
Author(s):  
J A Movshon ◽  
C Blakemore
Keyword(s):  
Spatial Frequency ◽  
Human Visual System ◽  
Human Vision ◽  
Orientation Tuning ◽  
High Contrast ◽  
Vertical Grating ◽  
Spatial Frequencies ◽  
High Contrast Grating ◽  
Orientation Specificity ◽  
Adaptation Method

An adaptation method is used to determine the orientation specificity of channels sensitive to different spatial frequencies in the human visual system. Comparison between different frequencies is made possible by a data transformation in which orientational effects are expressed in terms of equivalent contrast (the contrast of a vertical grating producing the same adaptational effect as a high-contrast grating of a given orientation). It is shown that, despite great variances in the range of orientations affected by adaptation at different spatial frequencies (±10° to ±50°), the half-width at half-amplitude of the orientation channels does not vary systematically as a function of spatial frequency over the range tested (2·5 to 20 cycles deg−1). Two subjects were used and they showed significantly different orientation tuning across the range of spatial frequencies. The results are discussed with reference to previous determinations of orientation specificity, and to related psychophysical and neurophysiological phenomena.

Spatio-Chromatic Information Content of Natural Scenes

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 162-162 ◽  
Author(s):  
T Troscianko ◽  
C A Parraga ◽  
G Brelstaff ◽  
D Carr ◽  
K Nelson
Keyword(s):  
Spatial Frequency ◽  
Human Vision ◽  
Natural Scenes ◽  
Data Set ◽  
Frequency Space ◽  
Sensitivity Functions ◽  
Visual Encoding ◽  
Spatial Frequencies ◽  
Hyper Spectral ◽  
Fourier Spectra

A common assumption in the study of the relationship between human vision and the visual environment is that human vision has developed in order to encode the incident information in an optimal manner. Such arguments have been used to support the 1/f dependence of scene content as a function of spatial frequency. In keeping with this assumption, we ask whether there are any important differences between the luminance and (r/g) chrominance Fourier spectra of natural scenes, the simple expectation being that the chrominance spectrum should be relatively richer in low spatial frequencies than the luminance spectrum, to correspond with the different shape of luminance and chrominance contrast sensitivity functions. We analysed a data set of 29 images of natural scenes (predominantly of vegetation at different distances) which were obtained with a hyper-spectral camera (measuring the scene through a set of 31 wavelength bands in the range 400 – 700 nm). The images were transformed to the three Smith — Pokorny cone fundamentals, and further transformed into ‘luminance’ (r+g) and ‘chrominance’ (r-g) images, with various assumptions being made about the relative weighting of the r and g components, and the form of the chrominance response. We then analysed the Fourier spectra of these images using logarithmic intervals in spatial frequency space. This allowed a determination of the total energy within each Fourier band for each of the luminance and chrominance representations. The results strongly indicate that, for the set of scenes studied here, there was no evidence of a predominance of low-spatial-frequency chrominance information. Two classes of explanation are possible: (a) that raw Fourier content may not be the main organising principle determining visual encoding of colour, and/or (b) that our scenes were atypical of what may have driven visual evolution. We present arguments in favour of both of these propositions.

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