Surfaces In Natural Scenes Research Articles

Little information loss with red-green color deficient vision in natural environments

Inherited color vision deficiency affects red-green discrimination in about one in twelve men from European populations. Its effects have been studied mainly in primitive foraging but also in detecting blushing and breaking camouflage. Yet there is no obvious relationship between these specific tasks and vision in the real world. The aim here was to quantify the impact of color vision deficiency by estimating computationally the information available to observers about colored surfaces in natural scenes. With representative independent sets of 50 and 100 hyperspectral images, estimated information was found to be only a little less in red-green color vision deficiency than in normal trichromacy. Colorimetric analyses revealed the importance of large lightness variations within scenes, small redness-greenness variations, and uneven frequencies of different colored surfaces. While red-green color vision deficiency poses challenges in some tasks, it has much less effect on gaining information from natural environments.

Natural Scene Statistics and Distance Perception: Ground Surface and Non-ground Objects

Both natural scene statistics and ground surfaces have been shown to play important roles in visual perception, in particular, in the perception of distance. Yet, there have been surprisingly few studies looking at the natural statistics of distances to the ground, and the studies that have been done used a loose definition of ground. Additionally, perception studies investigating the role of the ground surface typically use artificial scenes containing perfectly flat ground surfaces with relatively few non-ground objects present, whereas ground surfaces in natural scenes are typically non-planar and have a large number of non-ground objects occluding the ground. Our study investigates the distance statistics of many natural scenes across three datasets, with the goal of separately analyzing the ground surface and non-ground objects. We used a recent filtering method to partition LiDAR-acquired 3D point clouds into ground points and non-ground points. We then examined the way in which distance distributions depend on distance, viewing elevation angle, and simulated viewing height. We found, first, that the distance distribution of ground points shares some similarities with that of a perfectly flat plane, namely with a sharp peak at a near distance that depends on viewing height, but also some differences. Second, we also found that the distribution of non-ground points is flatter and did not vary with viewing height. Third, we found that the proportion of non-ground points increases with viewing elevation angle. Our findings provide further insight into the statistical information available for distance perception in natural scenes, and suggest that studies of distance perception should consider a broader range of ground surfaces and object distributions than what has been used in the past in order to better reflect the statistics of natural scenes.

Color improves edge classification in human vision.

Despite the complexity of the visual world, humans rarely confuse variations in illumination, for example shadows, from variations in material properties, such as paint or stain. This ability to distinguish illumination from material edges is crucial for determining the spatial layout of objects and surfaces in natural scenes. In this study, we explore the role that color (chromatic) cues play in edge classification. We conducted a psychophysical experiment that required subjects to classify edges into illumination and material, in patches taken from images of natural scenes that either contained or did not contain color information. The edge images were of various sizes and were pre-classified into illumination and material, based on inspection of the edge in the context of the whole image from which the edge was extracted. Edge classification performance was found to be superior for the color compared to grayscale images, in keeping with color acting as a cue for edge classification. We defined machine observers sensitive to simple image properties and found that they too classified the edges better with color information, although they failed to capture the effect of image size observed in the psychophysical experiment. Our findings are consistent with previous work suggesting that color information facilitates the identification of material properties, transparency, shadows and the perception of shape-from-shading.

Predicting frequency of metamerism in natural scenes by entropy of colors

Estimating the frequency of metameric surfaces in natural scenes usually requires many comparisons of surface colors to determine which are visually indistinguishable under one light but distinguishable-by a certain criterion degree-under another. The aim here was to test the predictive power of a simpler approach to estimation based on the entropy of colors. In simulations with 50 hyperspectral images of natural scenes, the logarithm of the observed relative frequency of metamerism in each scene under two successive daylights was regressed on combinations of the estimated Shannon differential entropies of the colors of the scene under the same two daylights. The regression was strong, and it remained so when restricted to the estimated differential entropy under just the first daylight, providing that the criterion degree of metamerism was limited. When the criterion degree was made more extreme, however, the restricted regression failed. A possible explanation of the predictive power of differential entropy is briefly considered.

Number of perceptually distinct surface colors in natural scenes

The ability to perceptually identify distinct surfaces in natural scenes by virtue of their color depends not only on the relative frequency of surface colors but also on the probabilistic nature of observer judgments. Previous methods of estimating the number of discriminable surface colors, whether based on theoretical color gamuts or recorded from real scenes, have taken a deterministic approach. Thus, a three-dimensional representation of the gamut of colors is divided into elementary cells or points which are spaced at one discrimination-threshold unit intervals and which are then counted. In this study, information-theoretic methods were used to take into account both differing surface-color frequencies and observer response uncertainty. Spectral radiances were calculated from 50 hyperspectral images of natural scenes and were represented in a perceptually almost uniform color space. The average number of perceptually distinct surface colors was estimated as 7.3 × 10(3), much smaller than that based on counting methods. This number is also much smaller than the number of distinct points in a scene that are, in principle, available for reliable identification under illuminant changes, suggesting that color constancy, or the lack of it, does not generally determine the limit on the use of color for surface identification.

Estimating the information available from colored surfaces in natural scenes

The information preserved in identifying surfaces solely by their color can be quantified by measures defined by Shannon, including capacity and mutual information. The aim of this study was (1) to determine whether the capacity of an additive Gaussian channel provides an asymptotic upper bound to mutual information estimated for natural scenes under different illuminants, and (2) to explore the effect of different color representations on mutual-information estimates.

Image/source statistics of surfaces in natural scenes

Image/source statistics of surfaces in natural scenes

Surface segmentation based on the luminance and color statistics of natural scenes.

The luminance and color of surfaces in natural scenes are relatively independent under certain linear transformations, with the luminance of a surface providing little information about the color of that surface, and vice versa. However, differences in luminance between two locations in a natural scene remain strongly associated with differences in color. We used the statistics of the spatiochromatic structure of natural scenes as the priors for a Bayesian model that decides whether or not two points within an image fall on the same surface. This model provides a biologically plausible algorithm for surface segmentation that models observer segmentations well.

Image/source statistics of surfaces in natural scenes

Perceiving surfaces in a manner that accords with their physical properties is essential for successful behaviour. Since, however, a given retinal image can have been generated by an infinite variety of natural surfaces with different geometrical and/or physical qualities, the corresponding percepts cannot be determined by the stimulus per se. Rather, resolution of this quandary requires a strategy of vision that incorporates the statistical relationship of the information in retinal images to its sources in representative environments. To examine this probabilistic relationship with respect to the features of object surfaces, we analysed a database of range images in which the distances of all the objects in a series of natural scenes were measured with respect to the image plane by a laser range scanner. By taking any particular scene obtained in this way to be made up of a set of concatenated surface patches, we were able to explore the statistics of scene roughness, size–distance relationships, surface orientation and local curvature, as well as the independent components of natural surfaces. The relevance of these statistics to both perception and the neuronal organization of the underlying visual circuitry is discussed.

Surface segmentation based on the luminance and color statistics of natural scenes

Received January 29, 2002; revised manuscript received September 12, 2002; accepted September 25, 2002The luminance and color of surfaces in natural scenes are relatively independent under certain linear trans-formations, with the luminance of a surface providing little information about the color of that surface, andvice versa. However,

Detecting natural changes of cone-excitation ratios in simple and complex coloured images.

Ratios of excitations in each cone-photoreceptor class produced by light reflected from pairs of surfaces in a scene are almost invariant under natural illuminant changes. The stability of these spatially defined ratios may explain the remarkable ability of human observers to efficiently discriminate illuminant changes from changes in surface reflectances. Spatial cone-excitation ratios are not, however, exactly invariant. This study is concerned with observers' sensitivity to these invariance violations. Simulations of Mondrian paintings with either 49 or two natural surfaces under Planckian illuminants were presented as images on a computer-controlled display in a two-interval experimental design: in one interval, the surfaces underwent an illuminant change; in the other interval, the surfaces underwent the same change but the images were then corrected so that, for each cone class, ratios of excitations were preserved exactly. Although the intervals with corrected images corresponded individually to highly improbable natural events, observers systematically misidentified them as containing the illuminant changes, the probability of error increasing as the violation of invariance in the other interval increased. For the range of illuminants and surfaces tested, sensitivity to violations of invariance was found to depend on cone class: it was greatest for long-wavelength-sensitive cones and least for short-wavelength-sensitive cones. Spatial cone-excitation ratios, or some closely related quantities, seem to be the cues preferred by observers for making inferences about surface illuminant changes.

Spectral properties of V4 neurons in the macaque

Spectral properties of 129 cells in the V4 area of 5 macaque monkeys were studied quantitatively with narrow-band and broad-band colored lights. The large majority of cells exhibited some degree of wavelength sensitivity within their receptive fields. The half-bandwidth of the primary peak in the spectral-response curve was less than 50 nm for 72% of the cells; the mean half-bandwidth of these cells, 27 nm, is similar to that found for color-opponent ganglion cells and cells in the parvocellular dorsal lateral geniculate nucleus (dLGN). Contrast-response functions indicated that the narrow spectral tuning of these cells derived from cone opponent interactions. From comparison of receptive-field sizes, we suggest that a typical V4 neuron sums inputs that ultimately derive from several thousand ganglion or parvocellular dLGN cells. In spite of their wavelength sensitivity, most V4 cells had properties that would not fit some simple criteria for classification as "color selective." First, few cells showed overt signs of color opponency, namely, on-inhibition or off-excitation to spectrally opponent wavelengths. Second, about 30% of the cells in V4 had spectral-response curves with 2 peaks. (The wavelength distribution of these second peaks was almost identical to that of primary peaks, and combinations of peak wavelengths were fairly random.) Third, most cells responded to white light; overall, the response to white light was about 60% of that to the best narrow-band or broad-band colored light. Similarly, most V4 cells gave at least a small response to all or nearly all of the different broad-band colored lights we presented. Therefore, a given V4 cell is very likely to respond to most of the colored or white surfaces in natural scenes. These combinations of response properties probably explain the widely divergent percentages of "color" cells reported in previous studies of V4. The most unusual spectral property we found in V4 was a large, spectrally sensitive surround outside the "classical receptive field" of most cells. Although stimulation of the surround by itself did not cause any response, surround stimulation could completely suppress the response to even the optimally colored stimulus in the receptive field. In general, the optimal wavelengths for receptive-field excitation and surround suppression were the same or nearly so. Thus, "color contrast" may be computed in V4. In some cases, contrasting wavelengths in the surround caused moderate enhancement of response to a receptive-field stimulus.(ABSTRACT TRUNCATED AT 400 WORDS)