三维图像重构的参数估计与算法实现

论文介绍了一种SFS算法的参数估计及其实现。它在考虑自遮掩影响的情况下,有效地估计了SFS算法中涉及的各种控制参数,并引入亮度约束、灰度梯度约束和可积性约束,计算出表面高度和表面向量,实现三维重构。最后还指出了在Matlab中实现需要注意的问题。     

三维表面重构过程参数的估计研究

在SFS(基于阴影的三维表面重构)各种算法中,都需要光源方向及曲面反射率。这些参数与图像的恢复效果息息相关。针对这些参数,也应运而生了各种各样的行之有效的方法。论文讨论了两种有效的算法,利用合成图像分别进行试验,并对其优缺点和运行效率进行比较,从而为进一步的SFS算法研究提供有价值的参考。     

基于SFS原理的SMT焊点表面三维重构技术研究

基于明暗重构形状原理重构表面组装焊点的表面三维形状过程是:先通过图像采集设备,采集到SMT焊点图像,使用相关的图像处理技术,对SMT焊点图像进行处理;根据一个确定的反射模型建立物体表面形状与图像亮度之间的约束关系和物体表面形状的先验知识建立物体表面形状参数的约束关系,然后对这些约束关系联立求解,可得到物体表面的三维形状.同时针对不可接受SMT焊点图像重构出的三维图像不够理想的缺点进行了改进.在阐述其基本思想和原理的基础上,结合实例介绍了该技术的实现方法与步骤,对其中焊点图像的获取与处理、焊点三维重构技术算法等主要内容与关键技术进行了研究和探讨,并对结果进行了分析验证.     

基于阴影的三维表面重构技术的概述

三维表面重构是计算机视觉的主要任务之一。已经发展了各种各样的重构技术,其中利用单幅图像中物体表面明暗变化来恢复其表面形状的技术尤其引人注目,这不仅因为该技术简单易于实现,更重要的是它适用于各种其它方法难以应用的场合,例如,在机载、星载、弹载环境中的利用。论文从SFS问题的研究背景出发,介绍并分析这方面的一些重要进展,通过对各种重构方法的分析与比较,提出SFS问题的研究发展趋势和一些值得研究的方向。     

单幅图像三维表面重建的算法研究与实现

以单幅二维图像为研究对象,应用SFS的基本理论,首先对二维图像进行了光照的倾角和偏角的估计,在光源坐标系下计算出物体每一点的表面梯度;之后,旋转坐标系,在观察坐标系下得到物体的表面梯度;最后采用三点辛普森公式求出物体表面的高度。整个算法流程简单,求解层次分明,重构满意度较高。     

基于SFS方法的三维重构及精度分析

从明暗恢复形状(SFS)是计算机视觉中三维重构问题的研究热点和难点之一,目前已有算法存在两个问题:1)选择的反射模型不符合物体表面的反射特性;2)引入的约束条件和求解过程过于复杂,求解速度慢,效率低。对SFS算法进行了详细分析,引入了朗伯特光照反射模型,对物体表面做球形假设,然后对图像做近似微分运算以求出高度函数,实现了利用单幅灰度图像恢复物体表面三维形状并仿真的数据处理方法,同时对传统线性化SFS算法和所提算法进行了实验验证,对两种模型的重构精度和算法的执行效率进行了比较和分析。实验仿真结果表明,在保证一定精度的前提下,所提算法的执行效率比传统算法高。     

SFS方法及其与立体视觉方法的集成方案综述

立体视觉(StereoVision)方法是目前利用图象数据获取物体三维信息的主要方法之一。但该方法在图象灰度变化较缓慢的区域,由于难以准确地进行图象间的象素配对,而严重影响了它的效果。利用从明暗重构物体三维表面形状(ShapefromShading,简称SFS)的方法与该方法相结合,是改善重构结果的主要途径之一。文章通过分析SFS问题本身的不适定性,揭示了目前几类主要的SFS算法在可靠性、稳定性、局限性以及实用性方面所存在的问题,并在此基础上,简要地介绍了四类SFS与立体视觉方法相结合的形式,说明了通过利用立体视觉为SFS补充辅助的信息来消除SFS问题的不适定性,并对过去SFS的实现方法进行有效的改进,它是提高集成系统准确性的关键。     

基于SFS方法的三维表面重建算法研究

SFS(由明暗恢复形状)方法研究是计算机三维视觉研究领域中的一个重要分支.以朗伯体光照漫反射模型为基础,对物体表面图像明暗恢复其表面高度和梯度的抽象模型进行分析,研究基于朗伯体定律求解受光点梯度的算法及SFS方法的实现原理.     

2SFS方法及其与立体视觉方法的集成方案综述

立体视觉（Stereo Vision）方法是目前利用图象数据获取物体三维信息的主要方法之一。但该方法在图象灰度变化较缓慢的区域，由于难以准确地进行图象间的象素配对．而严重影响了它的效果。利用从明暗重构物体三维表面形状（Shape from Shading，简称SFS）的方法与该方法相结合，是改善重构结果的主要途径之一。文章通过分析SFS问题本身的不适定性，揭示了目前几类主要的SFS算法在可靠性、稳定性、局限性以及实用性方面所存在的问题，并在此基础上，简要地介绍了四类SFS与立体视觉方法相结合的形式．说明了通过利用立体视觉为SFS补充辅助的信息来消除SFS问题的不适定性，并对过去SFs的实现方法进行有效的改进．它是提高集成系统准确性的关键。     

点光源照明参数估计的算法研究

针对使用朗伯反射模型描述漫反射表面的形状恢复算法存在较大误差的问题,提出了一种偏角的计算和特定方向的选择相关的点光源参数估计的从明暗恢复形状的算法.首先假定摄像机采用正交投影,方向与光源方向一致,建立适合漫反射表面的图像辐照度方程,然后将方程转化为包含物体深度信息的偏微分方程,使用点光源估计方法应用于SFS技术的线性化方法,进而得到物体表面的三维形状.合成人脸的PGM格式的图像实验结果表明,采用改进的算法成像准确,且深度信息值域比较宽,效果满足要求.     

Shape-From-Silhouette Across Time Part II: Applications to Human Modeling and Markerless Motion Tracking

In Part I of this paper we developed the theory and algorithms for performing Shape-From-Silhouette (SFS) across time. In this second part, we show how our temporal SFS algorithms can be used in the applications of human modeling and markerless motion tracking. First we build a system to acquire human kinematic models consisting of precise shape (constructed using the temporal SFS algorithm for rigid objects), joint locations, and body part segmentation (estimated using the temporal SFS algorithm for articulated objects). Once the kinematic models have been built, we show how they can be used to track the motion of the person in new video sequences. This marker-less tracking algorithm is based on the Visual Hull alignment algorithm used in both temporal SFS algorithms and utilizes both geometric (silhouette) and photometric (color) information.Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.     

基于分形的从明暗恢复形状算法研究

从明暗恢复形状是计算机视觉领域中的经典病态问题，传统方法是通过引入光滑约束等条件来获得问题的解，但传统方法存在因过平滑而失真的缺点。针对传统方法恢复结果的局限性，提出了一种基于分形约束的从明暗恢复形状的新算法，该方法首先给出分形约束条件，之后结合反射图线性化与最小能量法来计算出曲面高度。该方法不仅克服了传统算法因基于光滑假设所造成的恢复结果过分平滑而失真的缺点，且不需要可积性的约束条件，也不需要对边界条件的假设，实验结果表明，该方法用于自然景物的三维表面重构，可获得比传统方法更好的恢复效果。     

Efficient fair algorithms for message communication

A computer network serves distributed applications by communicating messages between their remote ends. Many such applications desire minimal delay for their messages. Beside this efficiency objective, allocation of the network capacity is also subject to the fairness constraint of not shutting off communication for any individual message. Processor Sharing (PS) is a de facto standard of fairness but provides significantly higher average delay than Shortest Remaining Processing Time (SRPT), which is an optimally efficient but unfair algorithm. In this paper, we explore efficient fair algorithms for message communication where fairness means that no message is delivered later than under PS. First, we introduce a slack system to characterize fair algorithms completely and develop efficient fair algorithms called Pessimistic Fair Sojourn Protocol (PFSP), Optimistic Fair Sojourn Protocol (OFSP), and Shortest Fair Sojourn (SFS). Then, we prove that a fair online algorithm does not assure minimal average delay attainable with fairness. Our analysis also reveals lower bounds on worst-case inefficiency of fair algorithms. We conduct extensive simulations for various distributions of message sizes and arrival times. During either temporary overload or steady-state operation, SFS and other newly proposed fair algorithms support SRPT-like efficiency and consistently provide much smaller average delay than PS.     

Hierarchical Scheduling for Symmetric Multiprocessors

Hierarchical scheduling has been proposed as a scheduling technique to achieve aggregate resource partitioning among related groups of threads and applications in uniprocessor and packet scheduling environments. Existing hierarchical schedulers are not easily extensible to multiprocessor environments because 1) they do not incorporate the inherent parallelism of a multiprocessor system while resource partitioning and 2) they can result in unbounded unfairness or starvation if applied to a multiprocessor system in a naive manner. In this paper, we present hierarchical multiprocessor scheduling (H-SMP), a novel hierarchical CPU scheduling algorithm designed for a symmetric multiprocessor (SMP) platform. The novelty of this algorithm lies in its combination of space and time multiplexing to achieve the desired bandwidth partition among the nodes of the hierarchical scheduling tree. This algorithm is also characterized by its ability to incorporate existing proportional-share algorithms as auxiliary schedulers to achieve efficient hierarchical CPU partitioning. In addition, we present a generalized weight feasibility constraint that specifies the limit on the achievable CPU bandwidth partitioning in a multiprocessor hierarchical framework and propose a hierarchical weight readjustment algorithm designed to transparently satisfy this feasibility constraint. We evaluate the properties of H-SMP using hierarchical surplus fair scheduling (H-SFS), an instantiation of H-SMP that employs surplus fair scheduling (SFS) as an auxiliary algorithm. This evaluation is carried out through a simulation study that shows that H-SFS provides better fairness properties in multiprocessor environments as compared to existing algorithms and their naive extensions.     

三维重建中曲面反照率值的估计与应用

从单幅图像获得物体的表面高度是计算机视觉中的一个重要研究领域,其中一种重要的方法就是从明暗恢复形状(ShapefromShading,简称SFS)。在SFS的各种不同算法中都需要曲面的反照率值,反照率值的估算是否准确直接影响了三维重建的效果。针对反照率值的估算,已经产生了很多有效的算法。文中讨论了三种反照率值的估计算法及其优缺点,并将局部反照率估计算法引入到三维重建中,解决了由全局反照率值重建的弊端。     

Shape from Shading and Photometric Stereo Using Surface Approximation by Legendre Polynomials

In this paper, a new iterative shape from shading (SFS) algorithm is proposed. In the proposed algorithm, the given 3D surface is approximated by Legendre polynomials and the relationships between the given surface and its derivatives are represented in matrix forms using a polynomial coefficient vector. Then the relative depth and its derivatives are iteratively computed by updating the coefficient vector. Also the proposed SFS algorithm is extended to a photometric stereo case. In the proposed photometric stereo algorithm, the reflectance map is linearized and the cost function expressed in quadratic matrix form is minimized. The relative depth and its derivatives are also obtained by updating them iteratively. Performance of the proposed SFS and photometric stereo algorithms is evaluated in terms of three different error measures: the brightness error, orientation error, and height error. In addition, a performance comparison of the proposed and conventional SFS algorithms is shown.     

基于照明参数与反射系数的分层SFS算法1)

提出一种由明暗信息复原形状的鲁棒算法.此算法能有效地估计照明方向、漫反射系数、照明天顶角以及沿图像轮廓由明暗信息复原形状,并且在考虑自阴影影响情况下,用新方法从图像的统计特征估计照明的仰角和表面反射系数,使重建强度梯度接近输入图像梯度实现平滑约束.该方法为数据驱动,稳定可靠,能同时更新表面斜率与高度图,大大减小发射项与可积分项内的剩余误差.最后给出SFS(Shape from Shading)算法的分层实现.     

随机聚焦搜索优化算法

提出了一种新的优化算法: 随机聚焦搜索. 该算法属于群集智能, 它模仿了人类的搜索行为及其在搜索过程中的随机性, 算法简单并且计算复杂度小. 在对一系列典型复杂函数的优化测试中, 通过与差分进化算法和全面学习的粒子群算法进行对比, 验证了该算法性能. 仿真结果表明, 该算法能解决大多数benchmark函数问题, 并且有较快的寻优速度, 可以在一定程度上替代现有的优化算法.