基于激光点云多条件约束的相机检校方法

为了克服相机检校对二维/三维检校场的依赖,提出一种基于激光点云多条件约束的相机检校方法。该方法通过对相机获取的多视影像进行光束法平差获得初始相机参数;利用影像点云与其最邻近的激光点云之间的位置关系,以共线方程为基础模型,建立多条件约束的相机检校数学模型;使用不等式约束的最小二乘方法平差迭代解算相机参数。将本文方法与基于三维控制场的检校精度进行了实验对比分析,结果表明本文方法与基于三维控制场的检校精度相当,两者反投影平均误差相差小于0.1 pixel,验证了本文方法在没有传统检校场的情况下进行相机检校的可行性。     

Autostereoscopic display based on multi-layer lenticular lens

An autostereoscopic display based on a multi-layer lenticular lens was proposed. The autostereoscopic display is composed of a flat panel display and a multi-layer lenticular lens sheet, which includes a traditional single lenticular lens and an additional transparent layer. The focal length of the multi-layer lenticular lens can be changed by choosing the refractive index of the additional transparent layer to adjust the stereoscopic depth, and it is an easier and much more flexible way than changing lenticular lens's curvature radius. A 22-in. 9-view autostereoscopic display prototype was developed. The image crosstalk of the prototype is very small and it has good presentation to several viewers without wearing eyeglasses.     

Kernel-aligned multi-view canonical correlation analysis for image recognition

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.     

消除自由立体显示串扰的定向背光源设计

定向背光柱透镜光栅式多视点自由立体显示器的串扰和亮度均匀性问题是影响其广泛应用的主要因素之一.本文通过分析串扰产生的原因,根据偏轴光线的成像光学原理,推导出液晶显示屏光开关板列单元与柱透镜组成的偏轴光学系统的参数计算式.提出一种液晶显示屏光开关蝶形单元结构,设计了采用该蝶形单元的定向背光自由立体显示背光源模组.模拟仿真结果表明:所设计的定向背光自由立体显示背光源可消除自由立体显示的串扰;在视点平面90%观看区域的串扰度小于0.5%,光束10%峰值宽度的串扰面积比γ=0,光束90%峰值宽度比普通液晶单元结构的增大37%,改善了不同位置观看图像亮度变化大的问题.     

多视点自由立体显示串扰分析与视区仿真

对多视点自由立体显示的串扰成因进行了分析,推导出了无串扰观看点与光栅参数之间的关系;然后分别对垂直于显示器方向和平行于显示器方向的串扰进行了分析与计算,得到了屏前三维空间的串扰分布情况.仿真结果表明,沿显示屏幕垂直方向的视区范围大于水平方向的视区范围,而且在水平平行于屏幕的方向上,每一个视点存在多个理论无串扰观测点,但在垂直于屏幕方向上,每个视点只存在一个理论无串扰观测点.最后通过对屏前三维区域视点光强进行仿真,进一步验证了串扰计算的准确性,为自由立体显示系统的最佳设计与具体实现奠定了仿真基础.     

Learning the geometry of common latent variables using alternating-diffusion

One of the challenges in data analysis is to distinguish between different sources of variability manifested in data. In this paper, we consider the case of multiple sensors measuring the same physical phenomenon, such that the properties of the physical phenomenon are manifested as a hidden common source of variability (which we would like to extract), while each sensor has its own sensor-specific effects (hidden variables which we would like to suppress); the relations between the measurements and the hidden variables are unknown. We present a data-driven method based on alternating products of diffusion operators and show that it extracts the common source of variability. Moreover, we show that it extracts the common source of variability in a multi-sensor experiment as if it were a standard manifold learning algorithm used to analyze a simple single-sensor experiment, in which the common source of variability is the only source of variability.     

一种基于区域分割与跟踪的多视点视频校正算法

针对多视点图像系统中视点图像颜色不一致的问题,提出了基于区域分割与跟踪的多视点视频校正算法.采用3维高斯混合模型来描述颜色信息,利用期望-最大算法来精炼模型参量,并通过自适应聚类和概率平滑操作来得到精确的分割图像.这样建立起多视点图像分割区域间的局部映射关系,并利用这种局部映射关系对全局图像进行校正.最后利用视频跟踪技术实现对视频图像的校正.实验结果表明,该算法能消除颜色过饱和或颜色区域混淆的影响,且具有较好的校正效果.     

An integration method for scanned multi-view range images (MRIs) based on local weighted least squares (LWLS) surface fitting

We present a novel integration method that can fuse registered partially overlapping multi-view range images (MRIs) into a single-layer, smooth and detailed point set surface. A maximum likelihood criterion is developed to detect overlapping points in MRIs. Subsequently, the detected overlapping points are shifted onto a series of piecewise smooth local weighted least squares (LWLS) surfaces to remove bad influence of scanning noises, outliers and large gaps/registration errors. The LWLS surface is fitted in background neighborhood which contains sufficient information to reconstruct local surface accurately. And the shifting operation is done in a concentric tiny neighborhood which contains corresponding overlapping points. Finally, a simple procedure is designed to identify and merge those corresponding overlapping points. The novel method has the advantages of robust to large gaps/registration errors, possessing least squares means and uniform density distribution. Furthermore, the novel method is efficient since only overlapping points are processed and the non-overlapping points are remained as they are. Several state of the art integration methods were employed for comparison study and the experimental results demonstrate the superiority of the novel method.     

基于自由曲面的裸眼3D显示柱透镜光栅设计

设计了一种用于裸眼3D显示屏的低串扰新型双面柱透镜光栅板,光栅板的入射面为等间距排列的与显示器子像素数量相同的凹形自由曲面光栅结构,出射面为斜置的凸形自由曲面光栅结构.根据裸眼3D显示原理和几何光学原理推导了双面光栅板的光栅单元自由曲面设计公式和光栅参数计算公式.通过MATLAB编程计算和SolidWorks软件建模得到光栅板模型.用TracePro软件对所设计的光栅板模型进行光线追迹仿真,结果表明:参数优化后的6视点斜置柱透镜双面光栅3D显示在最佳视角的图像串扰度为0.068%,与传统6视点斜置柱透镜光栅的最佳视角图像串扰度相比降低了2个数量级,并且在观看距离2 000mm~3 000mm范围内图像串扰度基本不变.