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.     

基于光源平移恢复物体法线和材质的算法

提出了一种基于光源平移恢复物体法线和材质的算法.光源平移时获得一组输入图像.根据输入图像将物体表面上的点分成纯漫反射点和混合点(包含漫反射和镜面反射的点).首先,根据镜面反射的原理求出混合点的法线.然后根据求出的法线用全局优化的迭代方法来恢复物体的材质.最后,根据不同输入图像中光源与物体的相对位置的不同,通过一个三角函数来恢复物体表面纯漫反射点的法线.分析对比实验结果和场景的真实参数表明,算法能够有效地恢复物体的法线和材质.     

Color constancy from blackbody illumination

We present a theoretical analysis of what we believe to be a new color constancy method that inputs two chromaticities of an identical surface taken under two blackbody illuminations. By using the Planck formula for modeling spectra of outdoor illumination and by assuming that a narrowband camera sensitivity function is sufficiently narrow, surface colors can be estimated mathematically. Experiments with simulation and real data have been conducted to evaluate the effectiveness of the method. The results showed that although this method is a perfect vehicle for simulation data, it produces significant errors with real data. A thorough investigation of the cause of errors indicates how important the assumptions on both blackbody illuminations and narrowband camera sensitivities are to the method. Finally, we discuss the robustness of our method and the limitation of solving color constancy using the illumination constraint.     

Temperature Measurement Technique for Stabilizing the Light Output of RGB LED Lamps

The efficiency of light-emitting-diode (LED) lights approaches that of fluorescent lamps. LED light sources find more applications than conventional light bulbs due to their compactness, lower heat dissipation, and real-time color-changing capability. Stabilizing the colors of red–green–blue (RGB) LED lights is a challenging task, which includes color light intensity control using switching-mode power converters, color point maintenance against LED junction temperature change, and limiting LED device temperature to prolong the LED lifetime. In this paper, we present a LED junction temperature measurement technique for a pulsewidth modulation diode forward current controlled RGB LED lighting system. The technique has been automated and can effectively stabilize the color without the need for using expensive feedback systems that involve light sensors. Performance in terms of chromaticity and luminance stability for a temperature-compensated RGB LED system will be presented.      

Performance Analysis and Evaluation of Geometric Parameters in Stereo Deflectometry

This paper presents a novel geometric parameters analysis to improve the measurement accuracy of stereo deflectometry. Stereo deflectometry can be used to obtain form information for freeform specular surfaces. A measurement system based on stereo deflectometry typically consists of a fringe-displaying screen, a main camera, and a reference camera. The arrangement of the components of a stereo deflectometry system is important for achieving high-accuracy measurements. In this paper, four geometric parameters of a stereo deflectometry system are analyzed and evaluated: the distance between the main camera and the measured object surface, the angle between the main camera ray and the surface normal, the distance between the fringe-displaying screen and the object, and the angle between the main camera and the reference camera. The influence of the geometric parameters on the measurement accuracy is evaluated. Experiments are performed using simulated and experimental data. The experimental results confirm the impact of these parameters on the measurement accuracy. A measurement system based on the proposed analysis has been set up to measure a stock concave mirror. Through a comparison of the given surface parameters of the concave mirror, a global measurement accuracy of 154.2 nm was achieved.     

Traffic light recognition

Abstract

An automatic traffic light recognition system is proposed in this paper so that car drivers have sufficient information to make a correct decision. This in turn facilitates the construction of an ITS (Intelligent Transportation System). The proposed method can be applied to movable cameras, is adaptable to environmental variations and is proper for real‐time implementation. It consists of two phases: traffic light detection and classification. The two phases are based on color, region and border information. At the detection stage, the RGB color space is first converted into the HSI color space so as to find those regions with specific colors of traffic lights. The morphology technology is employed to remove holes and noise. Region labeling is then involved to detect candidate regions of traffic lights. At the classification stage, border detection is employed to obtain region borders which together with region colors are matching features. In this study, circle and arrow traffic lights can both be coped with. Moreover, our method is adaptable to environmental variations in numbers of lights, types of lights and time slots. Various experiments have been conducted to demonstrate the effectiveness and practicability of the proposed method.     

Research on the application of single camera stereo vision sensor in three-dimensional point measurement

A single camera stereo vision sensor model based on planar mirror imaging is proposed for measuring a three-dimensional point. The model consists of a CCD camera and a planar mirror. Using planar mirror reflections of a scene, a picture with parallax is obtained by shooting the target object and its virtual image. This is equivalent to shooting the target object from different angles with the camera and the virtual camera in the planar mirror, so it has the function of binocular stereo vision. In addition, the measurement theory of the three-dimensional point is discussed. The mathematical model of a single camera stereo vision sensor is established, the intrinsic and extrinsic parameters are calibrated, and the corresponding experiment has been done. The experimental results show that the measuring method is convenient and effective; it also has the advantages of simple structure, convenient adjustment, and is especially suitable for short-distance measurement with high precision.     

Part segmentation from stereo

This article addresses the problem of segmenting objects into parts using stereo images. There are three components in the part segmentation process: surface segmentation, region grouping, and volumetric models (superquadrics) recovery/segmentation. The surface segmentation process segments the image into a set of regions such that each region represents a smooth surface. The region grouping process merges the segmented regions into parts. Finally the process of volumetric models recovery/segmentation recovers the part model and segments that part into smaller parts if necessary. Because we use both surface and volumetric models to drive the part segmentation process, we can capture more geometric properties of the object, and the application domain of our approach is broader than that of previous approaches. The performance of the proposed system is demonstrated with real images and synthetic images. Experimental results show that the system is stable and capable of handling a variety of objects.©1994 John Wiley & Sons Inc     

Three-dimensional image sensing by chromatic confocal microscopy

In the image of a confocal microscope, only those parts of an object appear bright that are located in the focal plane of the objective. Because of an axial chromatic aberration deliberately introduced into the microscope objective, the location of the focal plane depends on the wavelength used. By using a white-light source and examining an object with a depth variation less than the axial range of the chromatic focus, we find that all parts of the object appear sharp and bright in the image, but according to its height they appear in different colors. A camera with black-and-white film sequentially combines, with three selected chromatic filters, intensity and tone of color of each object point. For each tone of color one can assign a height by using a calibration curve. This assignment could be made unequivocal by the selection of filters with adequate chromatic transmission.     

基于双目相机的光栅立体印刷图像合成

目的研究一种基于双目相机的光栅立体图像合成方法。方法首先用双目相机采集场景中2个观察角度的二维平面图像数据。基于双目立体视觉理论,采用一种鲁棒性较强的基于区域分割的图像匹配方法,得到精度较高的深度图。然后分析序列视差图像的成像模型,建立一种基于双目图像对生成序列视差图像的方法,得到连续角度等间隔的序列图像。最后基于柱镜光栅的光学特性形成的立体印刷图像编码规则,对序列视差图像进行纵向条纹抽样分割,等间隔的抽取每幅序列视差图像中的对应列实现光栅立体图像的合成。结果验证了该光栅立体图像合成方法的有效性。结论基于双目相机的光栅立体合成方法,可以使立体印刷产品实现个性化、便捷化的即时输出。     

A 3D Measurement Method Based on Coded Image

The binocular stereo vision system is often used to reconstruct 3D point clouds of an object. However, it is challenging to find effective matching points in two object images with similar color or less texture. This will lead to mismatching by using the stereo matching algorithm to calculate the disparity map. In this context, the object can’t be reconstructed precisely. As a countermeasure, this study proposes to combine the Gray code fringe projection with the binocular camera as well as to generate denser point clouds by projecting an active light source to increase the texture of the object, which greatly reduces the reconstruction error caused by the lack of texture. Due to the limitation of the camera viewing angle, a one-perspective binocular camera can only reconstruct the 2.5D model of an object. To obtain the 3D model of an object, point clouds obtained from multiple-view images are processed by coarse registration using the coarse SAC-IA algorithm and fine registration using the ICP algorithm, which is followed by voxel filtering fusion of the point cloud. To improve the reconstruction quality, a polarizer is mounted in front of the cameras to filter out the redundant reflected light. Eventually, the 3D model and the dimension of a vase are obtained after calibration.     

The ability of color defectives to judge signal lights at sea.

Measures were made of the ability of color-defective men to judge correctly the colors of navigation lights (red, green, or white) presented to them at night under realistic sea conditions. Eighty-one color-defective men were employed; they were categorized as to type and degree of defect using a battery of five color-vision tests. While the average performance of the color-defective men was considerably poorer than that of 24 color normals, there were large individual differences within each category of defect. Attempts to account for these differences in performance by variations in acuity, intelligence, and motivation failed. The extent to which the data can be accounted for by modern color-vision theory is discussed.     

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.     

一种基于全维视觉与前向单目视觉的目标定位算法

在机器人足球中,机器人周围的环境信息被颜色特殊化。在对基于颜色阈值分割的分析基础上,针对不同光线的情况下提出了改进的颜色阈值分割法,实现了全维视觉和前向单目视觉对场上不同颜色目标的识别。在此基础上实现了前项单目视觉和全维视觉的目标定位,利用Kalman滤波算法,实现二者的信息融合,从而实现更准确的目标定位信息。实验结果表明基于此算法可以弥补中远距离的目标信息的准确度,证明本算法是可行且有效的。     

Application of modulation measurement profilometry to objects with surface holes

We present a detailed discussion of how modulation measurement profilometry (MMP) is applied to measuring an object that has holes on its surface. MMP is not based on the conventional three-dimensional profilometry method with structured light triangulation but on modulation measurements; it has the advantage of measuring the surface of a test object by perpendicular projection, that is, the direction of the CCD camera is the same as that of the projecting light, and the wrapped phases need not be calculated. Thus the difficulties from shadows and spatial discontinuities in phase measurement profilometry and Fourier transform profilometry methods do not exist in MMP. Here we measure a wheellike object that is 31.5 mm thick with an outer diameter of 80 mm and an inner diameter of 20 mm. All the object seen with the CCD camera can be measured including the hole. Experimental results prove that this method is useful for three-dimensional profilometry measurement, especially for objects with discontinuous height steps and spatial isolation.     

Data input model for virtual reality-aided facility layout

An approach to automatically extract three dimensional (3D) models (that is, geometries and topologies) of physical objects in a facility is described. The rationale for this work is its repeated use in efficiently developing databases of 3D objects for applying virtual reality (VR) tools in detailed layout decision support. Obtaining 3D object models can be a challenging task. Sometimes they are available, for example, in a Computer-Aided Design (CAD) database and these can be readily imported into a VR database. But on many occasions one is not so fortunate and these object models have to be created in correlation to an existing or proposed facility, which can be an extremely tedious and time consuming task. A time efficient and economical alternative is to use video camera images, but quickly and accurately capturing the depth information from 2D camera images has so far remained elusive because the existing methodologies are too general purpose and operate at a lower level of abstraction, namely digitized images. We have developed a method for directly inputting 3D objects into VR-aided facility layout models, by integrating the strengths of previously tried and tested technological components: (i) camera calibration; (ii) image processing; (iii) stereo vision; and (iv) Delaunay triangulation. The techniques described here are embedded in a prototype architecture and toolkit called MIRRORS (Methodology for Inputting Raw Recordings into 3D Object Renderings for Stereo). The primary contribution of this paper is that it has been able to design an integrated system to build 3D object models from 2D images. The MIRRORS system has been primarily designed for objects without free-form surfaces and whose shape can be recovered from a relatively nondense set of points.     

主动多基线立体视觉及其在机器人焊接技术中的应用研究

介绍了多基线立体视觉的基本原理针对焊接工件等一类表面缺纹纹理物体的三维深度恢复这一难题,提出采用基于条纹光照明方式的主动多基立体视觉方法解决这类物体的三维视觉建模问题,并通过验证明方法的有效性和可靠性。     

Computer-generated holograms of a real three-dimensional object based on stereoscopic video images

A novel method of using stereoscopic video images to synthesize the computer-generated hologram (CGH) patterns of a real 3D object is proposed. Stereoscopic video images of a real 3D object are captured by a 3D camera system. Disparity maps between the captured stereo image pairs are estimated and from these estimated maps the depth data for each pixel of the object can be extracted on a frame basis. By using these depth data and original color images, hologram patterns of a real object can be computationally generated. In experiments, stereoscopic video images of a real 3D object, a wooden rhinoceros doll, are captured by using the Wasol 3D adapter system and its depth data are extracted from them. Then, CGH patterns of 1280 pixels x 1024 pixels are generated with these depth-annotated images of the wooden rhinoceros doll, and the CGH patterns are experimentally displayed via a holographic display system.     

Spatial processing in color reproduction

We consider the reproduction of color subject to material and neighborhood constraints. By "material constraints," we mean any constraints that are applied to the amount of ink, lights, voltages, and currents that are used in the generation of color. In the first instance we consider the problem of reproducing a target color constrained by maximum additive color signals, such as in the phosphorescence process in a cathode ray tube. In the second instance we consider the more difficult problem of reproducing color subject to constraints on the maximum primary color variations in a (spatial) neighborhood. We introduce the idea of adjacent color variance (ACV) and then attempt to reproduce colors subject to an upper bound on the ACV. An algorithm that is suitable for this task is the method of vector space projections (VSP). In order to use VSP for constrained color reproduction, we use a novel approach to linearize nonlinear CIE-Lab space constraints. Experimental results are furnished that demonstrate that using the ACV as a bound helps to reduce reproduction artifacts in a color image.     

Spectral sharpening with positivity

Spectral sharpening is a method for developing camera or other optical-device sensor functions that are more narrowband than those in hardware, by means of a linear transform of sensor functions. The utility of such a transform is that many computer vision and color-correction algorithms perform better in a sharpened space, and thus such a space can be used as an intermediate representation for carrying out calculations. In this paper we consider how one may sharpen sensor functions such that the transformed sensors are all positive. We show that constrained optimization can be used to produce positive sensors in two fundamentally different ways: by constraining the coefficients in the transform or by constraining the functions directly. In the former method, we prove that convexity can be used to constrain the solution exactly. In a sense, we are continuing the work of MacAdam and of Pearson and Yule, who formed positive combinations of the color-matching functions. However, the advantage of the spectral sharpening approach is that not only can we produce positive curves, but the process is "steerable" in that we can produce positive curves with as good or better properties for sharpening within a given set of sharpening intervals. At base, however, it is positive colors in the transformed space that are the prime objective. Therefore we also carry out sharpening of sensor curves governed not by positivity of the curves themselves but of colors resulting from them. Curves that result have negative lobes but generate positive colors. We find that this type of constrained sharpening generates the best results, which are almost as good as for unconstrained sharpening but without the penalty of negative colors. All methods discussed may be used with any number of sensors.