Biomedical subjects
Publications and source records attributed to Thorsten Hansen.
We asked human observers to adjust the color of natural fruit objects until they appeared achromatic. The objects were generally perceived to be gray when their color was shifted away from the observers' gray point in a direction opposite to the typical color of the fruit. These results show that color sensations are not determined by the incoming sensory data alone, but are significantly modulated by high-level visual memory.
We used a noise-masking paradigm to investigate the number and properties of chromatic mechanisms involved in image segmentation. Observers were presented with a pattern of dynamic random squares, each independently modulated along a certain direction in DKL color space, either in the isoluminant plane or in the L - M luminance plane. A signal consisting of a rectangular region of squares, oriented horizontally or vertically, was added to the noise. The signal squares were spatially and temporally aligned to the noise squares, excluding the possibility of phase offsets to mediate segmentation performance. Noise and signal color directions were independently varied, and the signal contrast was measured at which an observer could reliably indicate the orientation of the signal. In a second set of experiments, the noise was simultaneously varying in two directions, symmetrically arranged around the signal direction. Masking was generally highest when signal and noise were modulated along the same direction and minimal for orthogonal noise. No difference was found between signals modulated along cardinal directions or intermediate directions. However, measured tuning widths critically depended on the type of noise: Noise modulated along one direction results in narrow tuning, whereas two-sided noise results in broad tuning. A chromatic detection model with multiple broadly tuned mechanisms successfully accounts for the experimental findings, both for narrow and broad tuning curves. Models with four broadly tuned cardinal mechanisms or multiple narrowly tuned mechanisms failed to reproduce the data. Our results suggest an important role for multiple, broadly tuned mechanisms in image segmentation.
We compared the visual detection thresholds for cone-isolating stimuli of trichromats (those with normal color vision) with those of X-linked dichromats, who lack either the long-wavelength-sensitive (L) cones (protanopes) or middle-wavelength-sensitive (M) cones (deuteranopes). At low (1 Hz) temporal frequencies, dichromats have significantly higher (twofold) thresholds for all colored stimuli than trichromats; whereas at high (16 Hz) temporal frequencies, they perform as well or better than trichromats. The advantages of dichromats in detecting high temporally modulated targets can be related to an increased number, through replacement, of the remaining L- or M-cone type. However, their disadvantages in detecting low temporally modulated targets, even in directions of color space where their increased number of cone photoreceptors might be expected to be beneficial, are best explained in terms of the loss of L-M cone opponency and the inability of the visual pathways to reorganize to allow the detection of low-frequency luminance modulation.
Filling-in models were successful in predicting psychophysical data for brightness perception. Nevertheless, their suitability for real-world image processing has never been examined. A unified architecture for both predicting psychophysical data and real-world image processing would constitute a powerful theory for early visual information processing. As a first contribution of the present paper, we identified three principal problems with current filling-in architectures, which hamper the goal of having such a unified architecture. To overcome these problems we propose an advance to filling-in theory, called BEATS filling-in, which is based on a novel nonlinear diffusion operator. BEATS filling-in furthermore introduces novel boundary structures. We compare, by means of simulation studies with real-world images, the performance of BEATS filling-in with the recently proposed confidence-based filling-in. As a second contribution we propose a novel mechanism for encoding luminance information in contrast responses ('multiplex contrasts'), which is based on recent neurophysiological findings. Again, by simulations, we show that 'multiplex contrasts' at a single, high-resolution filter scale are sufficient for recovering absolute luminance levels. Hence, 'multiplex contrasts' represent a novel theory addressing how the brain encodes and decodes luminance information.
The number and nature of the mechanisms for the detection of colored stimuli are still unclear. We use the paradigm of classification images to investigate the detection of a signal of homogeneous color added to a noisy texture. Both signal and noise colors were chosen from the isoluminant plane of the Derrington-Krauskopf-Lennie (DKL) color space. The signal consisted of a square of homogeneous color that was chosen from either cardinal or noncardinal directions of the DKL color space. The noisy texture consisted of small squares of varying colors that were chosen randomly across the isoluminant plane. Classification images reveal that (1) the cardinal axes play no specific role; (2) the widths of the tuning curves vary between 30 and 90 deg, consistent with the variation of tuning widths of neurons at early cortical stages; and (3) detection is not based on the whole region covered by the signal but is influenced mostly by a small spot around the fixation point.
Junctions provide important cues in various perceptual tasks, such as the determination of occlusion relationships for figure-ground separation, transparency perception, and object recognition, among others. In computer vision, junctions are used in a number of tasks, like point matching for image tracking or correspondence analysis. We propose a biologically motivated approach to junction representation in which junctions are implicitly characterized by high activity for multiple orientations within a cortical hypercolumn. A local measure of circular variance is suggested to extract junction points from this distributed representation. Initial orientation measurements are often fragmented and noisy. A coherent contour representation can be generated by a model of V1 utilizing mechanisms of collinear long-range integration and recurrent interaction. In the model, local oriented contrast estimates that are consistent within a more global context are enhanced while inconsistent activities are suppressed. In a series of computational experiments, we compare junction detection based on the new recurrent model with a feedforward model of complex cells. We show that localization accuracy and positive correctness in the detection of generic junction configurations such as L- and T-junctions is improved by the recurrent long-range interaction. Further, receiver operating characteristics analysis is used to evaluate the detection performance on both synthetic and camera images, showing the superior performance of the new approach. Overall, we propose that nonlocal interactions implemented by known mechanisms within V1 play an important role in detecting higher-order features such as corners and junctions.
The extraction of oriented contrast information by cortical simple cells is a fundamental step in early visual processing. The orientation selectivity originates at least partly from the input of lateral geniculate nuclei neurons with properly aligned receptive fields. In the present article, we investigate the feedforward interactions between on- and off-pathways. Based on physiological evidence we propose a push-pull model with dominating opponent inhibition (DOI). We show that the model can account for empirical data on simple cells, such as contrast-invariant orientation tuning, sharpening of orientation tuning with increasing inhibition, and strong response decrements to stimuli with luminance gradient reversal. With identical parameter settings, we apply the model for the processing of synthetic and real world images. We show that the model with DOI can robustly extract oriented contrast information from noisy input. More important, noise is adaptively suppressed, i.e. the model simple cells do not respond to homogeneous regions of different noise levels, while remaining sensitive to small contrast changes. The image processing results reveal a possible functional role of the strong inhibition as observed empirically, namely to adaptively suppress responses to noisy input.
Assigning a basic color name to an object and rating the amount of a particular hue is a fundamental visual capability. Traditional color scaling studies have used increment flashes or isoluminant stimuli of a homogeneous color. Natural objects, however, do not contain a single color but are characterized by a distribution of different chromatic hues. Here we study color scaling using photographs of natural fruit objects. Stimuli were either homogeneous spots, digital photographs of fruit objects (e.g., banana), or outline shapes of the fruit objects. Stimuli were displayed on a CRT monitor on a homogeneous white background; its luminance was varied above and below the medium gray. The chromaticity of the stimuli was varied in 36 equally spaced chromatic directions in the isoluminant plane of the Derrington-Krauskopf-Lennie (DKL) color space. For each stimuli, subjects rated the amount of red, green, blue, and yellow in the stimulus on a scale from 0-8. In agreement with earlier studies we found that the positions of the peak ratings for each color do not coincide with the cardinal axis of DKL color space and are largely invariant under changes of the background luminance. For the average rating we found a dependence on background luminance for all colors: yellow ratings increase with darker backgrounds, whereas ratings for the other colors, in particular green, decrease. For the fruit objects, we found a selective increase in the average color rating for the natural fruit color. For example, the average rating for yellow was 1.7 times higher for the banana images compared to disc stimuli. No such selective increase was found for outline shapes. We conclude that the distribution of hues in natural objects with a characteristic object color can have a profound effect on color scaling and color appearance.