Self-calibration of stationary non-rotating zooming cameras

This paper proposes a new method for self-calibrating a set of stationary non-rotating zooming cameras. This is a realistic configuration, usually encountered in surveillance systems, in which each zooming camera is physically attached to a static structure (wall, ceiling, robot, or tripod). In particular, a linear, yet effective method to recover the affine structure of the observed scene from two or more such stationary zooming cameras is presented. The proposed method solely relies on point correspondences across images and no knowledge about the scene is required. Our method exploits the mostly translational displacement of the so-called principal plane of each zooming camera to estimate the location of the plane at infinity. The principal plane of a camera, at any given setting of its zoom, is encoded in its corresponding perspective projection matrix from which it can be easily extracted. As a displacement of the principal plane of a camera under the effect of zooming allows the identification of a pair of parallel planes, each zooming camera can be used to locate a line on the plane at infinity. Hence, two or more such zooming cameras in general positions allow the obtainment of an estimate of the plane at infinity making it possible, under the assumption of zero-skew and/or known aspect ratio, to linearly calculate the camera's parameters. Finally, the parameters of the camera and the coordinates of the plane at infinity are refined through a nonlinear least-squares optimization procedure. The results of our extensive experiments using both simulated and real data are also reported in this paper.     

Efficient disparity vector prediction schemes with modified P frame for 2D camera arrays

An efficient disparity estimation algorithm for multi-view video sequences, recorded by a two-dimensional camera array in which the cameras are spaced equidistantly, is presented. Because of the strong geometrical relationship among views, the disparity vectors of a certain view can for most blocks be derived from the disparity vectors of other views. A frame constructed using that idea is called a D frame in this work. Three new prediction schemes which contain D frames are proposed for encoding 5 × 3 multi-view video sequences. The schemes are applied to several multi-view image sequences taken from a camera-array and they are compared in terms of quality, bit-rate and complexity. The experimental results show that the proposed prediction schemes significantly decrease the complexity of the encoder at a very low cost of quality and/or bit-rate.     

A probabilistic approach for video stabilisation in compressed domain

Machine vision systems, which are being extensively used for intelligent transportation applications, such as traffic monitoring and automatic navigation, suffer from image instability caused by environment unstable conditions. On the other hand, by increasing the use of home video cameras which sometimes need to remove unwanted camera movement, which is created by cameraman shaking hands, video stabilisation algorithms are being considered. The video stabilisation process consists of three essential phases: global motion estimation, intentional motion estimation and motion compensation. Motion estimation process is the main time consuming part of global motion estimation phase. Using motion vectors extracted directly from MPEG compressed video, instead of any other special feature, can increase the algorithm generality. In addition, it provides the facility for integrating video stabilisation and video compression subsystems and removing the block matching phase from video stabilisation procedure. Elimination of any iterative outlier removal preprocessing and adaptive selection of motion vectors has increased speed of the algorithm. Although deterministic approaches are faster than the related probabilistic methods, they have essential problems in escaping from local optima. For this purpose, particle filters, the ability of which is considerable when submitted to non-linear systems with non-Gaussian noises, are utilised. Setting the parameters of the particle filter using a fuzzy control system reduces the incorrect intentional camera motion removal. The proposed method is simulated and applied to video stabilisation problem and its high performance on various video sequences is demonstrated.     

基于Photoshop Camera Raw插件的数码照片后期处理案例分析

Camera Raw插件自Photoshop CS4版本之后就附加在软件中,这大大方便了数码照片的后期处理。文章使用Photoshop CS6,通过实例展示Camera Raw插件调整JPEG格式照片的强大功能。     

基于立体视觉的人体头部目标识别研究

使用立体视觉系统可确定任意物体的三维轮廓及其表面任意点的位置信息和深度信息。本文将立体视觉运用于人体头部目标检测,设计了一种基于图像深度信息处理的人体头部识别系统。系统采用Xtion摄像机采集场景的深度图像,并对其进行特征分析,根据深度图像的特点以及头部的特征确定头部目标区域。再对目标区域采用Mean Shift算法进行聚类处理,得到清晰的图像边缘。最后通过基于动态阈值的一维熵函数分割法实现头部的分割识别。该系统可快速锁定目标区域,减少了算法的计算量,大大提高了系统的识别速度。此外,采集深度图像的xtion摄像机悬挂在目标场景的正上方,因而较好的解决了目前人体目标检测受遮挡的问题。经实验论证,该系统有较高的识别精度。     

产业园区高清卡口智能协同监控系统设计与实施

高清卡口智能协同监控系统采用数字高清摄像机采集进出入园区车辆的图像信息,通过公安专用光纤网络协同传输至公安局指挥中心的数字分析系统,进行信息协同处理和信息协同发布与更新,及时发现布控车辆及犯罪嫌疑人员,实现快速处置。     

视觉测量中摄像机标定精度评估方法

为了准确地评估摄像机标定结果的精确程度,在基于棋盘格长度的评估方法的基础上,提出了基于外参数的摄像机标定精度评估方法。该方法通过对同一组图像分两次进行标定,得到两个不同位置的世界坐标系,利用标定结果求得这两个世界坐标系原点的距离并与实际的距离作比较,能够比较形象地描述标定结果中外参数的精确程度,进而评价摄像机标定结果的精确程度。     

基于两条平行线段的摄像机标定

提出了一种基于空间平行线段的摄像机标定算法和理论. 1)当两条平行线段的比值已知时: a)该平行线段的$n$幅图像可提供关于摄像机内参数的2(n-1)个二次约束方程; b)如果图像极点同时也是已知的,则该平行线段的n幅图像可提供关于摄像机内参数的5n-6个约束方程,其中3(n-1)个为二次约束,n-2个为三次约束,n-1个为四次约束. 2)当两条平行线段的比值未知时,则该平行线段的n幅图像可提供关于摄像机内参数的2n-3个约束方程,其中n-2个为四次约束, n-1个为六次约束.在理论分析的基础上,本文给出了摄像机标定的具体算法.模拟实验和真实图像实验均证明了本文方法的可行性.另外,鉴于在很多真实场景中均存在平行线段,因此本文所得到的结果不仅具有理论意义而且也有一定的实用价值.     

3DS MAX中体视动画生成模块的实现

介绍了体视动画的生成方法，讨论了如何基于流行的三维动画软件3DS MAX开发体视动画的生成模块。最后给出了建筑动态模拟体视动画中的一帧图像。     

Optimal camera placement for accurate reconstruction

Three-dimensional (3D) measurements can be recovered from several views by triangulation. This paper deals with the problem of where to place the cameras in order to obtain a minimal error in the 3D measurements, also called camera network design in photogrammetry. We pose the problem in terms of an optimization design, dividing it into two main components: (1) an analytical part dedicated to the analysis of error propagation from which a criterion is derived, and (2) a global optimization process to minimize this criterion. In this way, the approach consists of an uncertainty analysis applied to the reconstruction process from which a covariance matrix is computed. This matrix represents the uncertainty of the detection from which the criterion is derived. Moreover, the optimization has discontinuities due to the presence of occluding surfaces between the viewpoint and the object point group, which leads to a combinatorial optimization process. These aspects are solved using a multi-cellular genetic algorithm. Experimental results are provided to illustrate the effectiveness and efficiency of the solution.