Estimating illuminant color based on luminance balance of surfaces

To accomplish color constancy the illuminant color needs to be discounted from the light reflected from surfaces. Some strategies for discounting the illuminant color use statistics of luminance and chromaticity distribution in natural scenes. In this study we showed whether color constancy exploits the potential cue that was provided by the luminance balance of differently colored surfaces. In our experiments we used six colors: bright and dim red, green, and blue, as surrounding stimulus colors. In most cases, bright colors were set to be optimal colors. They were arranged among 60 hexagonal elements in close-packed structure. The center element served as the test stimulus. The observer adjusted the chromaticity of the test stimulus to obtain a perceptually achromatic surface. We used simulated black body radiations of 3000 (or 4000), 6500, and 20000 K as test illuminants. The results showed that the luminance balance of surfaces with no chromaticity shift had clear effects on the observer's achromatic setting, which was consistent with our hypothesis on estimating the scene illuminant based on optimal colors.     

Photometric reconstruction of a dynamic textured surface from just one color image acquisition

Textured surface analysis is essential for many applications. We present a three-dimensional recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to measure the textured surfaces with a high degree of accuracy. For this, we use a color digital sensor and principles of color photometric stereo. This method uses a single color image, instead of a sequence of gray-scale images, to recover the surface of the three dimensions. It can thus be integrated into dynamic systems where there is significant relative motion between the object and the camera. To evaluate the performance of our method, we compare it on real textured surfaces to traditional photometric stereo using three images. We thus show that it is possible to have similar results with just one color image.     

Wavelength intervals selection of illumination for separating objects from backgrounds in color vision applications

For scenes with complicated environments, the object is hard to discriminate from a background of various colors in color vision applications. This paper presents a partial least squares (PLS) method for improving discrimination of colored surfaces by selecting appropriate spectral intervals for illumination from the visible spectrum. First, the reflectance functions of all the surfaces are calibrated by multiple standard references. Second, the spectral intervals with high variables important in projection (VIP) scores of PLS analysis are selected for LED illumination. Afterwards, by using the selected wavelength intervals of LEDs for experiment, surfaces of the captured image can be clearly distinguished. Compared with the images obtained under illumination of unselected wavelength intervals of LEDs, the discriminations of most surfaces are more effective. The experiment result demonstrates the usefulness of this method.     

Color line search for illuminant estimation in real-world scenes.

The estimation of illuminant color is mandatory for many applications in the field of color image quantification. However, it is an unresolved problem if no additional heuristics or restrictive assumptions apply. Assuming uniformly colored and roundly shaped objects, Lee has presented a theory and a method for computing the scene-illuminant chromaticity from specular highlights [H. C. Lee, J. Opt. Soc. Am. A 3, 1694 (1986)]. However, Lee's method, called image path search, is less robust to noise and is limited in the handling of microtextured surfaces. We introduce a novel approach to estimate the color of a single illuminant for noisy and microtextured images, which frequently occur in real-world scenes. Using dichromatic regions of different colored surfaces, our approach, named color line search, reverses Lee's strategy of image path search. Reliable color lines are determined directly in the domain of the color diagrams by three steps. First, regions of interest are automatically detected around specular highlights, and local color diagrams are computed. Second, color lines are determined according to the dichromatic reflection model by Hough transform of the color diagrams. Third, a consistency check is applied by a corresponding path search in the image domain. Our method is evaluated on 40 natural images of fruit and vegetables. In comparison with those of Lee's method, accuracy and stability are substantially improved. In addition, the color line search approach can easily be extended to scenes of objects with macrotextured surfaces.     

Case Study: Color Control in the Automotive Industry

Numerous manufacturers are concerned with ensuring the homogeneity in the colors of different parts that compose their products. This quality objective of primary importance is especially challenging when the parts are sourced by different suppliers. The technical difficulties are accentuated when metallic or pearly paints are used since the presence of metallic chips in the paint causes a variation in the color according to the angle of observation. Also, the development of metametric colors increases these difficulties even more, as these colors are sensitive to different illumination sources. The present study relates to the production of colored car bumpers. By using a spectrocolorimeter and color samples, the process is improved to better respect the customer specifications. To achieve this, critical colors and significant parameters affecting colors are identified, design of experiments is used to optimize the process settings, and a correlation study allows further process improvement. The results achieved are impressive: the quality index used has been improved by 67% in only 6 months. This case study therefore illustrates how simple quality tools can be used in a rigorous search for process improvement toward total color mastering with zero defect objective.     

基于激光诱导表面周期结构的微纳防伪结构色

激光诱导表面周期结构由于其周期相关的光栅衍射特性在明场下显示出鲜艳的结构色,备受研究人员的广泛关注,而微纳结构在显微镜暗场显示的颜色通常容易被忽略.本文报道通过飞秒激光对氧化铟锡薄膜加工形成双周期光栅结构,利用其在明场和暗场的观察下具有不同的颜色特性实现图像加密应用.通过控制飞秒激光的偏振、脉冲能量和扫描速度在氧化铟锡...     

Color from shape from color: a simple formalism with known light sources

Photometric stereo is a well-known technique for recovering surface normals of a surface but requires three or more images of a surface taken under illumination from different directions. At best, one may dispense with the need for multiple images by using colored lights tuned to camera filters. But a less restrictive paradigm is available that uses the orientation-from-color approach, wherein multiple broadband illuminants impinge on a surface simultaneously. In that method, colors for a Lambertian surface lie on an ellipsoid in color space. The method has been applied mainly to single-color objects, with ellipsoid quadratic-form parameters determined from a large number of pixels. However, recently Petrov and Antonova [Color Res. Appl. 21, 97 (1996)] developed an entirely local approach, useful also for multicolored objects with color uniform in each patch. We investigate to what extent a method such as that of Petrov and Antonova can be applied in the ostensibly simpler situation in which the complex lighting environment is known, i.e., a color photometric stereo situation, with all lights in play at once with only a single image to analyze. We find that, assuming a simple model of color formation, we are able to recover the object colors along with surface normals, using only a single image. Because we immerse the object in a known lighting environment, we show that only half of the equations utilized by Petrov and Antonova are actually needed, making the method more stable. Nevertheless, solutions do not exist at every pixel; instead we may determine a best estimate of patch color, using a robust estimator, and then apply that estimate throughout a patch. Results are shown to be quite good compared with ground truth. The simple color model can often be made to hold more exactly by transforming the color space into one corresponding to spectrally sharpened sensors, which are a matrix transform away from the actual camera sensors. In our study the reliability and accuracy of the normal vector and of the surface color recovery algorithm are improved by this straightforward transformation.     

Illumination estimation via thin-plate spline interpolation

Thin-plate spline interpolation is used to interpolate the chromaticity of the color of the incident scene illumination across a training set of images. Given the image of a scene under unknown illumination, the chromaticity of the scene illumination can be found from the interpolated function. The resulting illumination-estimation method can be used to provide color constancy under changing illumination conditions and automatic white balancing for digital cameras. A thin-plate spline interpolates over a nonuniformly sampled input space, which in this case is a training set of image thumbnails and associated illumination chromaticities. To reduce the size of the training set, incremental k medians are applied. Tests on real images demonstrate that the thin-plate spline method can estimate the color of the incident illumination quite accurately, and the proposed training set pruning significantly decreases the computation.     

Increments and decrements in color constancy

The present study examines whether increment-decrement asymmetries reported in a number of recent center-surround situations occur in more complex images as well. Subjects saw the CRT simulation of a whole uniformly illuminated array of foreground surfaces presented against a large background surface and, for a number of different viewing contexts, made achromatic settings over a wide range of luminance values. Three results emerged. First, subjects' achromatic loci did not fall on a single straight line in color space but rather fell on two separate lines intersecting at some point in this space. Second, the intersection points were not identical to but dependent largely on background color and showed only small effects of foreground colors. Third, cone signals that were decremental relative to the intersection point were more responsive to illuminant changes than cone signals that were incremental, the latter additionally showing some variation with foreground colors. The results are interpreted in terms of increment-decrement asymmetries. They suggest that these asymmetries occur in more complex images as well.     

Color constancy at a pixel

In computational terms we can solve the color constancy problem if device red, green, and blue sensor responses, or RGB's, for surfaces seen under an unknown illuminant can be mapped to corresponding RGB's under a known reference light. In recent years almost all authors have argued that this three-dimensional problem is too hard. It is argued that because a bright light striking a dark surface results in the same physical spectra as those of a dim light incident on a light surface, the magnitude of RGB's cannot be recovered. Consequently, modern color constancy algorithms attempt only to recover image chromaticities under the reference light: They solve a two-dimensional problem. While significant progress has been made toward achieving chromaticity constancy, recent work has shown that the most advanced algorithms are unable to render chromaticity stable enough so that it can be used as a cue for object recognition [B. V. Funt, K. Bernard, and L. Martin, in Proceedings of the Fifth European Conference on Computer Vision (European Vision Society, Springer-Verlag, Berlin, 1998), Vol. II, p. 445.] We take this reductionist approach a little further and look at the one-dimensional color constancy problem. We ask, Is there a single color coordinate, a function of image chromaticities, for which the color constancy problem can be solved? Our answer is an emphatic yes. We show that there exists a single invariant color coordinate, a function of R, G, and B, that depends only on surface reflectance. Two corollaries follow. First, given an RGB image of a scene viewed under any illuminant, we can trivially synthesize the same gray-scale image (we simply code the invariant coordinate as a gray scale). Second, this result implies that we can solve the one-dimensional color constancy problem at a pixel (in scenes with no color diversity whatsoever). We present experiments that show that invariant gray-scale histograms are a stable feature for object recognition. Indexing on invariant distributions supports almost perfect recognition for a dataset of 11 objects viewed under five colored lights. In contrast, object recognition based on chromaticity histograms (post-color constancy preprocessing) delivers much poorer recognition.     

Role of color memory in successive color constancy

We investigate color constancy for real 2D paper samples using a successive matching paradigm in which the observer memorizes a reference surface color under neutral illumination and after a temporal interval selects a matching test surface under the same or different illumination. We find significant effects of the illumination, reference surface, and their interaction on the matching error. We characterize the matching error in the absence of illumination change as the "pure color memory shift" and introduce a new index for successive color constancy that compares this shift against the matching error under changing illumination. The index also incorporates the vector direction of the matching errors in chromaticity space, unlike the traditional constancy index. With this index, we find that color constancy is nearly perfect.     

A practical approach for estimating illumination distribution from shadows using a single image

This article presents a practical method that estimates illumination distribution from shadows using only a single image. The shadows are assumed to be cast on a textured, Lambertian surface by an object of known shape. Previous methods for illumination estimation from shadows usually require that the reflectance property of the surface on which shadows are cast be constant or uniform, or need an additional image to cancel out the effects of varying albedo of the textured surface on illumination estimation. But, our method deals with an estimation problem for which surface albedo information is not available. In this case, the estimation problem corresponds to an underdetermined one. We show that the combination of regularization by correlation and some user‐specified information can be a practical method for solving the underdetermined problem. In addition, as an optimization tool for solving the problem, we develop a constrained Non‐Negative Quadratic Programming (NNQP) technique into which not only regularization but also multiple linear constraints induced by user‐specified information are easily incorporated. We test and validate our method on both synthetic and real images and present some experimental results. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 143–154, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20047     

环境光及iPad背光源亮度设置对iPad呈色的影响

目的研究环境光以及i Pad的亮度设置对i Pad颜色特性的影响。方法测量3种常规光照环境下,14个不同的设备亮度设置下i Pad屏幕上显示色块的绝对色度值,分析不同环境光下不同设备亮度设置下i Pad的色温、对比度、通道可加性、色品恒定性以及色域等颜色特性。结果当i Pad的设备亮度小于175 cd/m2时,环境光对色温、对比度、通道可加性、色品恒定性以及色域等颜色特性有较大影响;当i Pad的设备亮度大于175 cd/m2时,环境光对i Pad的显色性能的影响较小;相同环境下,设备亮度的变化对i Pad色域以及色温产生较大影响,对其他颜色特性影响较小,设备亮度较大时,暗调部分的颜色被压缩。结论在一般环境光下,当背光源亮度设置大于175 cd/m2范围时,i Pad的显色性能较好较稳定,但部分暗调颜色被压缩。     

Importance of color in the segmentation of variegated surfaces

We examined how variations in color and brightness are used by the visual system in distinguishing textured surfaces that differed in their first- or second-order statistics. Observers viewed a 32 x 32 array containing two types of square elements differing in chromaticity or luminance or both. The spatial distributions of the two kinds of elements were varied within the array until observers could distinguish two juxtaposed regions. At low but not at high contrast, observers are better able to distinguish regions when the elements differ only in chromaticity than when they differ only in luminance. The advantage of color at low contrasts results from the greater visibility of the arrays defined by color variation. An observer's capacity to distinguish textures defined by variations in first-order chromatic statistics is little affected by the addition of achromatic noise but is more affected by the addition of chromatic noise. The relative robustness of chromatic cues in the face of achromatic noise leaves the visual system well equipped to exploit color variations in segmenting complex scenes, even in the presence of variations in brightness. This capacity seems to depend on mechanisms that sum over large regions: When surfaces differ in their second-order statistics and cannot be distinguished by mechanisms that sum over large regions, the advantage of color is much diminished.     

Neuartige dekorative Farbschichten durch Plasma‐Beschichtung

Novel decorative color coatings using plasma deposition The market does not stop to demand for novel products. Only those who offer innovative products will explore new segments of the market and will not loose against the cheap suppliers from far eastern countries. This is even more important in the field of surface technology. Many products would not be competitive without plasma technology. Companies changing surfaces with plasma technology expect a noticeable growth between 20 and 50 % within the next years [1]. The deposition of thin layers using plasma makes it possible to obtain highly brilliant color coatings, specially mixed color effects (rainbow like) as well as color changes depending of the observation angle. These optical special effects would not be feasible with common painting techniques. Thus plasma deposition opens a new field for surface coating. In these layers the colors are created via interference effects of the light being used for illumination. They are called interference colors, well known to the most of us from thin oil films on a wet street.     

Role of perceptual organization in chromatic induction

Color matches between two small patches were made in a display containing ten larger regions of different chromaticities. The spatial organization of the ten regions was varied while keeping constant the immediate surround of each patch as well as the space-average chromaticity of the entire stimulus. Different spatial arrangements were designed to alter the perceptual organization inferred by the observer without changing the ensemble of chromaticities actually in view. For example, one arrangement of the ten regions was consistent with five surfaces under two distinct illuminations, with one edge within the display (an "apparent illumination edge") dividing the stimulus into two areas, one under illuminant A and the other under illuminant C. Another spatial arrangement had the ten regions configured to induce an observer to infer ten surfaces under a single illumination. When the ten regions were arranged with an apparent illumination edge, the patch within the area of illuminant C was perceived as bluer than when the same patch and immediate surround were presented without an apparent illumination edge. The results are accounted for by positing that observers group together regions sharing the same inferred illumination, with a consequent effect on color perception: A fixed patch-within-surround shifts in hue and saturation toward the perceived illumination. We suggest that the change in color perception in a complex scene that results from a difference in real illumination may be caused by the inferred illumination at the perceptual level, not directly by the physical change in the light absorbed by photoreceptors.     

Characterization of natural illuminants in forests and the use of digital video data to reconstruct illuminant spectra

We describe illumination spectra in forests and show that they can be accurately recovered from recorded digital video images. Natural illuminant spectra of 238 samples measured in temperate forests were characterized by principal-component analysis. The spectra can be accurately approximated by the mean and the first two principal components. Compared with illumination under open skies, the loci of forest illuminants are displaced toward the green region in the chromaticity plots, and unlike open sky illumination they cannot be characterized by correlated color temperature. We show that it is possible to recover illuminant spectra accurately from digital video images by a linear least-squares-fit estimation technique. The use of digital video data in spectral analysis provides a promising new approach to the studies of the spatial and temporal variation of illumination in natural scenes and the understanding of color vision in natural environments.     

Manipulating substances with Physarum polycephalum

The plasmodium of Physarum polycephalum is a large single cell capable for optimal spanning of food sources and avoidance of harmful stimuli. The sophisticated foraging behavior of the plasmodium can be interpreted in terms of computation. When propagating on a substrate with distributed sources of food the plasmodium simulates a general-purpose storage modification machine, approximates varieties of proximity graphs and imitates calculation of shortest path and plane tessellation. The plasmodium's behaviour is determined by the space–time distribution of attracting and repelling sources, and immediately guided by the waves of excitation traveling inside the plasmodium. Due to cytoplasmic streaming a harmless colored substance can be naturally ingested by the plasmodium and distributed inside the protoplasmic network. We show that by controlling the plasmodium's propagation over an uncolored substrate we can ‘fill’ specified areas of the substrate with the color transported by the plasmodium. We experimentally demonstrate that the plasmodium of P. polycephalum excels in adaptive transportation, mixing and transformation of colored food particles. We uncover a range of operations implementable by the plasmodium over color set, and design methods to control mixing and transportation of colors.     

智能包装检测系统中光源波长研究

目的通过优化智能包装检测系统中的光源条件,提高多层包装中缺陷内衬纸智能检测的识别率和包装的良品率。方法在智能包装检测系统中,通过改变光源的波长来检测不同颜色的缺陷内衬纸,并对缺陷内衬纸面积检测的像素数量进行分析,来确定合适的光源波长。结果色相性导致红光、绿光及蓝光等可见单色光不能使相同颜色的内衬纸在智能包装检测系统中成像,但在白光、红外光和紫外光照射下可以检测出所有的缺陷内衬纸。进一步分析得知,智能包装检测系统在红外光照射下缺陷内衬纸的成像效果最好;在白光、红外光和紫外光照射下的识别率分别为90.5%, 99.5%, 96.5%,红外光照射下的识别率最高。结论在实际包装车间生产中,为使智能包装检测系统的准确率最高,可根据色相性选择光源或者直接选择红外光光源。