基于Voronoi图的自动白平衡算法

在数码相机系统中,不同色温下的图像存在偏色现象,而传统白平衡算法不能准确重现图像的真实颜色。为此,提出一种基于 Voronoi图的自动白平衡算法。利用Voronoi图统计白点数,估计当前环境的色温值,并做增益计算,根据标准值对图像颜色值进行校正。在不同背景和光源下的测试结果表明,该算法可使图像质量得到有效改善。     

Development of a Positioning Technique for an Urban Area Using Omnidirectional Infrared Camera and Aerial Survey Data

This paper describes an outdoor positioning system for vehicles that can be applied to an urban canyon by using an omnidirectional infrared (IR) camera and a digital surface model (DSM). By means of omnidirectional IR images, this system enables robust positioning in urban areas where satellite invisibility caused by buildings hampers high-precision GPS measurements. The omnidirectional IR camera can generate IR images with an elevation of 20–70° for the surrounding area of 360°. The image captured by the camera is highly robust to light disturbances in the outdoor environment. Through the IR camera, the sky appears distinctively dark; this enables easy detection of the border between the sky and the buildings captured in white due to the difference in the atmospheric transmittance rate between visible light and IR rays. The omnidirectional image, which includes several building profiles, is compared with building-restoration images produced by the corresponding DSM in order to determine the self-position. Field experiments in an urban area show that the proposed outdoor positioning method is valid and effective, even if high-rise buildings cause satellite blockage that affects GPS measurements.     

Color restoration method based on spectral information using normalized cut

This paper proposes a novel method for color restoration that can effectively apply accurate color based on spectral information to a segmented image using the normalized cut technique. Using the proposed method, we can obtain a digital still camera image and spectral information in different environments. Also, it is not necessary to estimate reflectance spectra using a spectral database such as other methods. The synthesized images are accurate and high resolution. The proposed method effectively works in making digital archive contents. Some experimental results are demonstrated in this paper.     

A Fast Display Method of Sky Colour Using Basis Functions

Computer graphics are being used for visual environmental assessment or architectural designs. Displaying the sky as a background is indispensable in generating photorealistic images for such applications. In this paper, we propose a fast display method of the sky colour by expressing the intensity distribution of the sky using basis functions, even if the sun position and/or the camera position are altered. In the proposed method, cosine functions are used as basis functions. The sun altitude is altered at certain intervals and the distributions of the sky colour for each sun altitude are precalculated and stored efficiently using basis functions. The colour of the sky in the view direction of an arbitrary sun position can be obtained from the stored distributions and displayed quickly. © 1997 by John Wiley & Sons, Ltd.     

Camera Motion Estimation Through Planar Deformation Determination

In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in the camera coordinate system. This condition is very restrictive but we show that, provided translation and depth inverse variations are small enough, the error on optical flow involved by the approximation of depths by a constant is small. In this context, we propose a new model of camera motion which allows to separate the image deformation in a similarity and a “purely” projective application, due to change of optical axis direction. This model leads to a quadratic approximation of image deformation that we estimate with an M-estimator; we can immediately deduce camera motion parameters. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.

基于单幅图像的视线计算研究

提出了一种新的基于单幅眼睛图像的视线计算方法。通过假定两眼的视线向量以及左右虹膜中心点组成的向量共面,可以计算得到两眼的视线方向。与现有的三维视线计算方法相比,提出的视线计算方法不需要眼角坐标等脸部特征信息,另外也不需要已知相机的焦距大小。可以允许相机的焦距大小以及与用户的距离自由改变。最后实验的结果也比较理想,充分证明了提出的视线计算方法的可行性。     

Shape estimation using polarization and shading from two views

This paper presents a novel method for 3D surface reconstruction that uses polarization and shading information from two views. The method relies on polarization data acquired using a standard digital camera and a linear polarizer. Fresnel theory is used to process the raw images and to obtain initial estimates of surface normals, assuming that the reflection type is diffuse. Based on this idea, the paper presents two novel contributions to the problem of surface reconstruction. The first is a technique to enhance the surface normal estimates by incorporating shading information into the method. This is done using robust statistics to estimate how the measured pixel brightnesses depend on the surface orientation. This gives an estimate of the object material reflectance function, which is used to refine the estimates of the surface normals. The second contribution is to use the refined estimates to establish correspondence between two views of an object. To do this, a set of patches are extracted from each view and are aligned by minimizing an energy functional based on the surface normal estimates and local topographic properties. The optimum alignment parameters for different patch pairs are then used to establish stereo correspondence. This process results in an unambiguous field of surface normals, which can be integrated to recover the surface depth. Our technique is most suited to smooth, non-metallic surfaces. It complements existing stereo algorithms since it does not require salient surface features to obtain correspondences. An extensive set of experiments, yielding reconstructed objects and reflectance functions, are presented and compared to ground truth.     

Analytical sensitivities for statistically extrapolated extreme load constraints in structural optimization

For some applications in structural optimization, it is required to have constraints on the extreme loads that represent long term loading conditions. This usually involves a statistical extrapolation procedure that fits maxima from simulated load time series to short term extreme value distributions and then extrapolates to an n-year return value. Often such situations are highly simplified because of the apparent complexity involved in evaluating the sensitivity of such constraints. However, such simplification is not necessary. In this study, we present a method to evaluate the sensitivities of such extrapolated extreme load constraints in a semi-analytical way. The method uses the implicit function theorem to obtain local derivatives at the points defined by the solution of the maximum likelihood estimate that is used to calculate the parameters of the short term extreme value distributions. Comparing with high accuracy finite difference estimates, the method is shown to give reasonably accurate values. We also demonstrate how the method can be used to estimate the uncertainty of the estimated n-year return value caused by uncertainty in both the maximum likelihood estimate and inherent uncertainties in the data. The method then is applied to a simple optimization example and shown to perform very well compared with using finite difference estimates for the sensitivities. Finally, we note that the method is in principle fairly general and could be applied to similar problems that do not specifically involve statistical extrapolation.     

Generalized mosaicing: wide field of view multispectral imaging

We present an approach to significantly enhance the spectral resolution of imaging systems by generalizing image mosaicing. A filter transmitting spatially varying spectral bands is rigidly attached to a camera. As the system moves, it senses each scene point multiple times, each time in a different spectral band. This is an additional dimension of the generalized mosaic paradigm, which has demonstrated yielding high radiometric dynamic range images in a wide field of view, using a spatially varying density filter. The resulting mosaic represents the spectrum at each scene point. The image acquisition is as easy as in traditional image mosaics. We derive an efficient scene sampling rate, and use a registration method that accommodates the spatially varying properties of the filter. Using the data acquired by this method, we demonstrate scene rendering under different simulated illumination spectra. We are also able to infer information about the scene illumination. The approach was tested using a standard 8-bit black/white video camera and a fixed spatially varying spectral (interference) filter.     

Accurately estimating reflectance parameters for color and gloss reproduction

A new method is described to estimate diffuse and specular reflectance parameters using spectral images, which overcomes the dynamic range limitation of imaging devices. After eliminating the influences of illumination and camera on spectral images, reflection values are initially assumed as diffuse-only reflection components, and subjected to the least squares method to estimate diffuse reflectance parameters at each wavelength on each single surface particle. Based on the dichromatic reflection model, specular reflection components are obtained, and then subjected to the least squares method to estimate specular reflectance parameters for gloss intensity and surface roughness. Experiments were carried out using both simulation data and measured spectral images. Our results demonstrate that this method is capable of estimating diffuse and specular reflectance parameters precisely for color and gloss reproduction, without requiring preprocesses such as image segmentation and synthesis of high dynamic range images.     

Estimation of a fluorescent lamp spectral distribution for color image in machine vision

We present a technique to quickly estimate the Illumination Spectral Distribution (ISD) in an image illuminated by a fluorescent lamp. It is assumed that the object colors are a set of colors for which spectral reflectances are available (in our experiments we use spectral measurements of 12 colors checker chart), the sensitivities of the camera sensors are known and the camera response is linear. Thus, the ISD can be approximated by a finite linear combinations of a small number of basis functions.     

A Direct Interpretation of Dynamic Images with Camera and Object Motions for Vision Guided Robot Control

A general scheme to represent the relation between dynamic images and camera and/or object motions is proposed for applications to visual control of robots. We consider the case where a moving camera observes moving objects in a static scene. The camera obtains images of the objects moving within the scene. Then, the possible combinations of the camera and the objects' poses and the obtained images are not arbitrary but constrained to each other. Here we represent this constraint as a lower dimensional hypersurface in the product space of the whole combination of their motion control parameters and image data. The visual control is interpreted as to find a path on this surface leading to their poses where a given goal image will be obtained. In this paper, we propose a visual control method to utilize tangential properties of this surface. First, we represent images with a composition of a small number of eigen images by using K-L (Karhunen-Loève) expansion. Then, we consider to reconstruct the eigen space (the eigen image space) to achieve efficient and straightforward controls. Such reconstruction of the space results in the constraint surface being mostly flat within the eigen space. By this method, visual control of robots in a complex configuration is achieved without image processing to extract and correspond image features in dynamic images. The method also does not need camera or hand-eye calibrations. Experimental results of visual servoing with the proposed method show the feasibility and applicability of our newly proposed approach to a simultaneous control of camera self-motion and object motions.     

Less restrictive camera odometry estimation from monocular camera

This paper addresses the problem of estimating a camera motion from a non-calibrated monocular camera. Compared to existing methods that rely on restrictive assumptions, we propose a method which can estimate camera motion with much less restrictions by adopting new example-based techniques compensating the lack of information. Specifically, we estimate the focal length of the camera by referring to visually similar training images with which focal lengths are associated. For one step camera estimation, we refer to stationary points (landmark points) whose depths are estimated based on RGB-D candidates. In addition to landmark points, moving objects can be also used as an information source to estimate the camera motion. Therefore, our method simultaneously estimates the camera motion for a video, and the 3D trajectories of objects in this video by using Reversible Jump Markov Chain Monte Carlo (RJ-MCMC) particle filtering. Our method is evaluated on challenging datasets demonstrating its effectiveness and efficiency.     

Spatiotemporal filtering of sequences of ultrasound images to estimate a dense field of velocities

Blood velocity estimation is required in many clinical applications. Ultrasonic imaging is often used to reach this goal. This article presents a velocity vector estimation method from ultrasonic imaging. It complements Doppler imaging, which has several limitations. New techniques such as block-matching (BM) and decorrelation-based methods have already been developed to overcome these limitations. Our method is based on spatiotemporal filtering to estimate the apparent velocity vector for each pixel of the sequence of ultrasound images. A moving object is represented by a group of pixels travelling from image to image in the sequence, leaving a trace in the spatiotemporal volume. A bank of filters was designed to estimate a local texture orientation related to the velocity of the object. The method was first developed in 2D then extended in 3D to estimate the two components in the imaging plane. The method was applied to sequences of ultrasound images of calibrated flow in a vessel (mean velocity ). The velocity estimates obtained in 2D and 3D showed mean errors less than 5% and 12%, respectively. The results are presented as dynamic cartography and dense fields of velocity vectors. The associated velocity profiles show good agreement with the theoretical parabolic profile of laminar flow. Our approach has been compared with three other velocity estimation methods and showed good performance in comparison with them.     

Guaranteed Energy Error Estimators for a Modified Robust Crouzeix–Raviart Stokes Element

This paper provides guaranteed upper energy error bounds for a modified lowest-order nonconforming Crouzeix–Raviart finite element method for the Stokes equations. The modification from Linke (Comput Methods Appl Mech Eng 268:782–800, 2014) is based on the observation that only the divergence-free part of the right-hand side should balance the vector Laplacian. The new method has optimal energy error estimates and can lead to errors that are smaller by several magnitudes, since the estimates are pressure-independent. An efficient a posteriori velocity error estimator for the modified method also should involve only the divergence-free part of the right-hand side. Some designs to approximate the Helmholtz projector are compared and verified by numerical benchmark examples. They show that guaranteed error control for the modified method is possible and almost as sharp as for the unmodified method.     

Assessing the influence of temperature variations on the geometrical properties of a low-cost calibrated camera system by using computer vision procedures

This paper estimates temperature influence on geometrical properties of both a single camera and a calibrated camera system, assuming low-cost CCD cameras. It does not cover the effect of temperature on the camera’s electronics. Firstly, the influence of temperature change on camera parameters was modelled and integrated into an existing analytical camera model. A modified camera model enables quantitative assessment regarding the influence of temperature variations for a single camera. Temperature variations also directly influence the accuracies of calibrated cameras. The inability to analytically determine the calibration method error magnitude, led us to experimentally estimate errors regarding calibrated cameras. Finally, the total error regarding calibrated cameras was derived by combining the numerical error of the calibration method with those errors originating from temperature variations. The results show that the influence of temperature variations decreases when increasing the distances of the observed objects from the cameras. On a typical building site, the temperature influence is reflected in the image as an error of less than one pixel.     

Map building through pseudo dense scan matching using visual sonar data

This paper presents a novel approach to the vision based grid map building and localization problem that works in a complex indoor environment with a single forward viewing camera. Most existing visual SLAM has been limited to the feature-based method and only a few researchers have proposed visual SLAM methods for building a grid map using a stereo vision system which has not been popular in practical application. In this paper, we estimate the planar depth by applying a simple visual sonar ranging technique to the single camera image and then associating sequential scans through our own pseudo dense adaptive scan matching algorithm reducing the processing time compared to the standard point-to-point correspondence based algorithm and finally produce a grid map. To this end, we construct a Pseudo Dense Scan (PDS) which is an odometry based temporal accumulation of the visual sonar readings emulating omni-directional sensing in order to overcome the sparseness of the visual sonar. Moreover, in order to obtain a much more refined map, we further correct the slight trajectory error incurred in the PDS construction step using Sequential Quadratic Programming (SQP) which is a well-known optimization scheme. Experimental results show that our method can obtain an accurate grid map using a single camera without the need for a high price range sensors or stereo camera.

A space–time spectral collocation method for the two-dimensional variable-order fractional percolation equations

In this article, we introduce a space–time spectral collocation method for solving the two-dimensional variable-order fractional percolation equations. The method is based on a Legendre–Gauss–Lobatto (LGL) spectral collocation method for discretizing spatial and the spectral collocation method for the time integration of the resulting linear first-order system of ordinary differential equation. Optimal priori error estimates in $L^2$ norms for the semi-discrete and full-discrete formulation are derived. The method has spectral accuracy in both space and time. Numerical results confirm the exponential convergence of the proposed method in both space and time.