Biomedical subjects

Frederick A A Kingdom

Publications and source records attributed to Frederick A A Kingdom.

17 recordsLinked to original sources

PubMed2006

Luminance-contrast properties of contour-shape processing revealed through the shape-frequency after-effect.

We investigated the first-order inputs to contour-shape mechanisms using the shape-frequency after-effect (SFAE), in which adaptation to a sinusoidally modulated contour causes a shift in the apparent shape-frequency of a test contour in a direction away from that of the adapting stimulus [Kingdom F. A. A., & Prins N. (2005a). Different mechanisms encode the shapes of contours and contour-textures. Journal of Vision 5(8), 463, (Abstract)]. We measured SFAEs for adapting and test contours (and edges) that differed in the contrast-polarity, scale (or blur) and magnitude of luminance contrast. The rationale was that if the SFAE was found to be reduced when adaptor and test differed along a particular dimension of luminance contrast, contour-shape mechanisms must be tuned to that dimension. Our results reveal that SFAEs manifest (i) a degree of selectivity to luminance contrast polarity for both even-symmetric (contours only) and odd-symmetric (both contours and edges) luminance profiles; (ii) a degree of selectivity to luminance scale (or blur); (iii) higher selectivity to fine compared to coarse scale for broadband edges (iv) a small preference for equal-in-contrast adaptors and tests. These results suggest that contour shapes are not encoded in the form of a sparse, cartoon-like sketch, as might be presumed by local energy (i.e. non-phase-selective) or form-cue invariant models, but instead in a form that is relatively 'feature-rich.'

Computer Graphics↗

PubMed2006

Direct evidence for the existence of energy-based texture mechanisms.

Two classes of models have been proposed to explain how the visual system processes texture modulations. In 'feature models', abstract representations of the featural properties of local texture regions (eg orientation, spatial frequency, contrast) are first generated, after which differences in individual feature properties across space are detected. In 'energy models', on the other hand, differences across space in the response energies of linear simple-cell-like filters are detected. This model thus processes the existing differences between texture regions directly without generating a full representation of the individual texture regions. We provide here direct evidence for the existence of the second, energy model, using an adaptation paradigm in conjunction with textures simultaneously modulated in two dimensions--orientation and spatial frequency. We found that the mechanism that processed the conjoint modulation was tuned to orientations and spatial frequencies that could not be predicted by any feature model, but which were precisely predicted by the energy model.

Differential Threshold↗

PubMed2006

Colour contrast influences perceived shape in combined shading and texture patterns.

The 'colour-shading effect' describes the phenomenon whereby a chromatic pattern influences perceived shape-from-shading in a luminance pattern. Specifically, the depth corrugations perceived in sinusoidal luminance gratings can be enhanced by spatially non-aligned, and suppressed by spatially aligned sinusoidal chromatic gratings. Here we examine whether colour contrast can influence perceived shape in patterns that combine shape-from-shading with shape-from-texture. Stimuli consisted of sinusoidal modulations of texture (defined by orientation), luminance and colour. When the texture and luminance modulations were suitably combined, one obtained a vivid impression of a corrugated depth surface. The addition of a colour grating to the texture-luminance combination was found to enhance the impression of depth when out-of-phase with the luminance modulation, and suppress the impression of depth when in-phase with the luminance modulation. The degree of depth enhancement and depth suppression was approximately constant across texture amplitude when measured linearly. In the absence of the luminance grating however, the colour grating had no phase-dependent affect on perceived depth. These results show that colour contrast modulates the contribution of shading to perceived shape in combined shading and texture patterns.

Color Perception↗

PubMed2005

Colour unmasks dark targets in complex displays.

Recent studies have suggested that colour (meaning chromatic) variations help the visual system segment luminance-variegated displays into their illumination and reflectance layers. This leads to the prediction that colour variations should unmask partially camouflaged achromatic transparencies on luminance-variegated backgrounds. We used 'Mondrian-like' backgrounds that were either achromatic, i.e., varying only in luminance, or chromatic, which in our stimuli meant varying in both luminance and colour. Both achromatic and chromatic backgrounds had the same luminance distribution. Thresholds for detecting simulated transparency targets were found to be lower when on the chromatic compared to achromatic backgrounds. We hypothesised that the chromatic-background advantage resulted from the extra cue provided by colour as to which borders were background and which transparency, predicting that (a) randomising the colours on either side of the transparency border, (b) rotating the target to destroy its X-junctions, and (c) viewing the target eccentrically, would each destroy the chromatic-background advantage. However, none of these predictions was upheld. We suggest therefore that the chromatic-background advantage is due to a low-level, rather than border-disambiguation mechanism. We suggest that chromatic variations reduce the noise, but not the signal, in the mechanism that detects dark targets in complex displays.

Color Perception↗

PubMed2005

Chromatic properties of the colour-shading effect.

The 'colour-shading effect' describes the phenomenon whereby chromatic variations affect the magnitude of perceived shape-from-shading in luminance patterns. A previous study showed that in mixed colour-plus-luminance sine-wave plaids, impressions of depth in the luminance component were enhanced by non-aligned chromatic components, and suppressed by aligned chromatic components [Nature Neuroscience 6 (2003) 641-644]. Here we examine the chromatic determinants of these effects. Colour contrast was defined along the cardinal axes of colour space in order to isolate the L-M and S-(L+M) post-receptoral chromatic mechanisms. We found no difference in the potency of L-M-only and S-(L+M)-only gratings, either for enhancing or suppressing perceived depth. Moreover, the magnitude of depth-suppression was no different for any combination of depth-enhancing and depth-suppressing cardinal directions. Finally we tested whether the visual system carried the assumption that natural shading is tinged with blue, by measuring perceived depth in a colour-plus-luminance grating that was made to appear either bright-yellow/dark-blue or bright-blue/dark-yellow. However there was no difference in the magnitude of depth-suppression between conditions, suggesting that the visual system does not make any assumption about the colour of natural shading. Taken together, the results suggest that while the colour-shading effect is highly sensitive to colour contrast, it is agnostic with respect to colour direction.

Color Perception↗

PubMed2005

Spatiochromatic statistics of natural scenes: first- and second-order information and their correlational structure.

Spatial filters that mimic receptive fields of visual cortex neurons provide an efficient representation of achromatic image structure, but the extension of this idea to chromatic information is at an early stage. Relatively few studies have looked at the statistical relationships between the modeled responses to natural scenes of the luminance (LUM), red-green (RG), and blue-yellow (BY) postreceptoral channels of the primate visual system. Here we consider the correlations among these channel responses in terms of pixel, first-order, and second-order information. First-order linear filtering was implemented by convolving the cosine-windowed images with oriented Gabor functions, whose gains were scaled to give equal amplitude response across spatial frequency to random fractal images. Second-order filtering was implemented via a filter-rectify-filter cascade, with Gabor functions for both first- and second-stage filters. Both signed and unsigned filter responses were obtained across a range of filter parameters (spatial frequency, 2-64 cycles/image; orientation, 0-135 degrees). The filter responses to the LUM channel images were larger than those for either RG or BY channel images. Cross correlations between the first-order channel responses and between the first- and second-order channel responses were measured. Results showed that the unsigned correlations between first-order channel responses were higher than expected on the basis of previous studies and that first-order channel responses were highly correlated with LUM, but not with RG or BY, second-order responses. These findings imply that course-scale color information correlates well with course-scale changes of fine-scale texture.

Algorithms↗

PubMed2004

Colour vision brings clarity to shadows.

We have revealed a new role for colour vision in visual scene analysis: colour vision facilitates shadow identification. Shadows are important features of the visual scene, providing information about the shape, depth, and movement of objects. To be useful for perception, however, shadows must be distinguished from other types of luminance variation, principally the variation in object reflectance. A potential cue for distinguishing shadows from reflectance variations is colour, since chromatic changes typically occur at object but not shadow boundaries. We tested whether colour cues were exploited by the visual system for shadow identification, by comparing the ability of human test subjects to identify simulated shadows on chromatically variegated versus achromatically variegated backgrounds with identical luminance compositions. Performance was superior with the chromatically variegated backgrounds. Furthermore, introducing random colour contrast across the shadow boundaries degraded their identification. These findings demonstrate that the visual system exploits inbuilt assumptions about the relationships between colour and luminance in the natural visual world.

Cognition↗

PubMed2004

A biologically inspired algorithm for the recovery of shading and reflectance images.

We present an algorithm for separating the shading and reflectance images of photographed natural scenes. The algorithm exploits the constraint that in natural scenes chromatic and luminance variations that are co-aligned mainly arise from changes in surface reflectance, whereas near-pure luminance variations mainly arise from shading and shadows. The novel aspect of the algorithm is the initial separation of the image into luminance and chromatic image planes that correspond to the luminance, red-green, and blue-yellow channels of the primate visual system. The red-green and blue-yellow image planes are analysed to provide a map of the changes in surface reflectance, which is then used to separate the reflectance from shading changes in both the luminance and chromatic image planes. The final reflectance image is obtained by reconstructing the chromatic and luminance-reflectance-change maps, while the shading image is obtained by subtracting the reconstructed luminance-reflectance image from the original luminance image. A number of image examples are included to illustrate the successes and limitations of the algorithm.

Algorithms↗

PubMed2003

Orientation opponency in human vision revealed by energy-frequency analysis.

Studies of second-order visual processing have primarily been concerned with understanding the mechanisms for detecting spatiotemporal variations in such attributes as contrast, orientation, spatial frequency, etc. Here, we have examined the orientation characteristics of second-order processes using bandpass noise whose Fourier energy is sinusoidally modulated across orientation, rather than across space or time. Sensitivity for detecting orientation-energy modulations was measured as a function of modulation frequency. The sensitivity function was bandpass, with a pronounced peak at an orientation frequency of 4 cycles/pi. An inverse Fourier transform of the sensitivity function revealed a filter profile displaying a centre-surround antagonism across orientation, with an excitatory centre within 6-9 deg and inhibitory lobes at 15-20 deg from the filter's centre. The degree of centre-surround antagonism increased with stimulus size far beyond the spatial range of the first-order filters (more than 64 times the dominant spatial wavelength of the noise carrier). These results suggest that second-order processing involves 'orientation-opponent' channels that extract differences in first-order outputs across orientation over a wide area of the visual field.

Contrast Sensitivity↗

PubMed2003

Color brings relief to human vision.

In natural scenes, chromatic variations, and the luminance variations that are aligned with them, mainly arise from surfaces such as flowers or painted objects. Pure or near-pure luminance variations, on the other hand, mainly arise from inhomogeneous illumination such as shadows or shading. Here, I provide evidence that knowledge of these color-luminance relationships is built into the machinery of the human visual system. When a pure-luminance grating is added to a differently oriented chromatic grating, the resulting 'plaid' appears to spring into three-dimensional relief, an example of 'shape-from-shading'. By psychophysical measurements, I found that the perception of shape-from-shading in the plaid was triggered when the chromatic and luminance gratings were not aligned, and suppressed when the gratings were aligned. This finding establishes a new role for color vision in determining the three-dimensional structure of an image: one that exploits the natural relationships that exist between color and luminance in the visual world.

Brain↗

PubMed2003

Detection and discrimination of texture modulations defined by orientation, spatial frequency, and contrast.

We sought to determine whether the detection and the identification of texture modulations are mediated by a common mechanism. On each trial two textures were presented, one of which contained a modulation in orientation (OM), spatial frequency (FM), or contrast (CM). Observers were required to indicate whether the modulated texture was presented in the first or the second interval as well as the nature of the texture modulation. The results showed that for two of the three pairwise matchings (OM-FM and OM-CM) detection and identification performance were nearly identical, suggesting a common underlying mechanism. However, when FM and CM textures were paired, discrimination thresholds were significantly higher than detection thresholds. In the context of the filter-rectify-filter model of texture perception, our results suggest that the mechanisms underlying detection are labeled with respect to their first-order input; i.e., the identities of these mechanisms are available to higher levels of processing. Several possible explanations for the misidentification of FM and CM at detection threshold are considered.

Contrast Sensitivity↗

PubMed2002

Scale invariance is driven by stimulus density.

Scale invariance refers to aspects of visual perception that remain constant with changes in viewing distance. Previously, Dakin and Herbert [Proc. Roy. Soc. B. 265 (1397) (1998) 659] reported that the spatial integration region (IR) for mirror symmetry in bandpass noise is scale invariant because its dimensions scale with the inverse of peak spatial frequency. In bandpass noise, however, peak spatial frequency covaries with stimulus numerosity (i.e. the total number of information samples) and density (i.e. the total number of information samples per unit area). In this study, we report four experiments that decoupled properties of the retinal image affected by viewing distance--spatial frequency, numerosity, size, and density--and measured their effect on IR size. Stimuli consisted of bandpass microelements with vertically mirror-symmetric but otherwise random positions, and we measured observer resistance to random jitter imposed on microelement position. Results show that jitter resistance and IR size vary with the inverse of stimulus density but are unaffected by changes in stimulus spatial frequency, numerosity, or size. We found the IR has a 2:1 height-to-width aspect ratio and integrates information from approximately 18 microelements regardless of their spatial separation. Our results reveal that stimulus density plays a central role in the visual system's implementation of scale invariance. Using an ideal-observer, we demonstrate that scale invariance reflects genuine neural scale selection rather than a physical limitation on the stimulus' information content. Our findings that jitter resistance and IR size vary with the inverse of density challenge current models of spatial vision but can be reconciled with a model that compares the output of bandpass non-Fourier mechanisms to select spatial scales that match stimulus density.

Humans↗

PubMed2002

Orientation- and frequency-modulated textures at low depths of modulation are processed by off-orientation and off-frequency texture mechanisms.

Intuitively it may seem likely that orientation-modulated (OM) and frequency-modulated (FM) textures are processed utilizing the first-order channels that are most responsive to the first-order (luminance) information contained in the textures. This assumption would imply that the detection or segmentation of OM or FM textures is accomplished by second-order mechanisms that receive their first-order input from neurons tuned to either the center, or to the peaks in the orientation and spatial-frequency distribution of the texture. Here we show that at low depths of modulation this is not the case. Using an adaptation paradigm, we show that the first-order filters involved in the perception of OM and FM textures are those which maximize the differential response between the different texture regions. Our explanation of this result is similar to that made by Regan and Beverley [J. Opt. Soc. Am. 73 (1983) 1684; J. Opt. Soc. Am. A 2 (1985) 147] for simple grating stimuli. However, we show that whereas Regan and Beverley's results could be accounted for on the basis of the tuning functions of the putative mechanisms involved, our results can be explained in terms of the characteristics of the textures themselves. Some implications of our finding are discussed.

Adaptation, Ocular↗

PubMed2002

Interactions between chromatic- and luminance-contrast-sensitive stereopsis mechanisms.

It is well known that chromatic information can assist in solving the stereo correspondence problem. It has also been suggested that there are two independent first-order stereopsis mechanisms, one sensitive to chromatic contrast and the other sensitive to luminance contrast (Vision Research 37 (1997) 1271). Could the effect of chromatic information on stereo correspondence be subserved by interactions between these mechanisms? To address this question, disparity thresholds (1/stereoacuity) were measured using 0.5 cpd Gabor patches. The stimuli possessed different relative amounts of chromatic and luminance contrast which could be correlated or anti-correlated between the eyes. Stereoscopic performance with these compound stimuli was compared to that with purely isoluminant and isochromatic stimuli at different contrasts. It was found that anti-correlated chromatic contrast severely disrupted stereopsis with achromatic stimuli and that anti-correlated luminance contrast severely disrupted stereopsis with chromatic stimuli. Less dramatic, but still significant, was the improvement in stereoacuity obtained using correlated colour and luminance contrast. These data are consistent with there being positive and negative interactions between chromatic and achromatic stereopsis mechanisms that take place after the initial encoding of disparity information, but before the extraction of stereoscopic depth. These interactions can be modelled satisfactorily assuming probability summation of depth sign information between independent mechanisms.

Color Perception↗

PubMed2002

Achromatic transparency and the role of local contours.

In this paper we investigate the role of contours and junctions in the perception of single-plane achromatic transparency. In order to measure the accuracy with which observers encode transparency, a six-luminance stimulus was employed in which the figural properties could be easily manipulated. Accuracy was measured by requiring subjects to select (either by the method of adjustment or by using a forced-choice procedure) the luminance that best completed a simulated transparent filter. The X junctions in the stimulus were destroyed or perturbed in three experiments. Simple occlusion of the junction (experiment 1), and perturbation of the orientation of the contours of the filter as they pass through the junction (experiment 3) resulted in small but significant reductions in performance. On the other hand, a sudden change in orientation of the background (material) contours (experiment 2) resulted in a small but significant enhancement of overall performance compared with the control stimulus. In the forced-choice task, reversals in the polarity of contours (as defined by the brightness order of flanking regions) around the junction were shown to effect large changes in subjects' accuracy in processing transparency. The overall results show that X and Psi junctions are indeed salient properties of transparent stimuli. The findings suggest that jagged contours with sudden changes in direction are more likely to be attributed to reflectance (material) changes than to changes due to a transparent filter (or to illumination).

Depth Perception↗

PubMedDate not supplied

Differential distributions of red-green and blue-yellow cone opponency across the visual field.

The color vision of Old World primates and humans uses two cone-opponent systems; one differences the outputs of L and M cones forming a red-green (RG) system, and the other differences S cones with a combination of L and M cones forming a blue-yellow (BY) system. In this paper, we show that in human vision these two systems have a differential distribution across the visual field. Cone contrast sensitivities for sine-wave grating stimuli (smoothly enveloped in space and time) were measured for the two color systems (RG & BY) and the achromatic (Ach) system at a range of eccentricities in the nasal field (0-25 deg). We spatially scaled our stimuli independently for each system (RG, BY, & Ach) in order to activate that system optimally at each eccentricity. This controlled for any differential variations in spatial scale with eccentricity and provided a comparison between the three systems under equivalent conditions. We find that while red-green cone opponency has a steep decline away from the fovea, the loss in blue-yellow cone opponency is more gradual, showing a similar loss to that found for achromatic vision. Thus only red-green opponency, and not blue-yellow opponency, can be considered a foveal specialization of primate vision with an overrepresentation at the fovea. In addition, statistical calculations of the level of chance cone opponency in the two systems indicate that selective S cone connections to postreceptoral neurons are essential to maintain peripheral blue-yellow sensitivity in human vision. In the red-green system, an assumption of cone selectivity is not required to account for losses in peripheral sensitivity. Overall, these results provide behavioral evidence for functionally distinct neuro-architectural origins of the two color systems in human vision, supporting recent physiological results in primates.

Color Perception↗

PubMedDate not supplied

Mechanism independence for texture-modulation detection is consistent with a filter-rectify-filter mechanism.

The ability of the visual system to detect stimuli that vary along dimensions other than luminance or color--"second-order" stimuli--has been of considerable interest in recent years. An important unresolved issue is whether different types of second-order stimuli are detected by a single, all purpose, mechanism, or by mechanisms that are specific to stimulus type. Using a conventional psychophysical paradigm, we show that for a class of second-order stimuli--textures sinusoidally modulated in orientation (OM), spatial frequency (FM), and contrast (CM)--the human visual system employs mechanisms that are selective to stimulus type. Whereas the addition of a subthreshold mask to a test pattern of the same stimulus type was found to facilitate the detection of the test, no facilitation was observed when mask and test were of different types, suggesting mechanism independence for the different types of stimulus. This finding raises the important question of whether mechanism independence is compatible with the well-known filter-rectify-filter (FRF) model of second-order stimulus detection, since FRF mechanisms, in principle, do not discriminate between stimulus types. We show that for all mask/test combinations except those with CM masks, the FRF mechanism giving the largest response to the test modulation is largely unaffected by subthreshold levels of a different stimulus-type mask. For this reason, we cannot rule out the possibility that FRF mechanisms mediate the detection of our stimuli. For combinations involving CM masks, however, we propose that a process of contrast normalization renders the test stimulus insensitive to the mask stimulus.

Contrast Sensitivity↗