Planar mirrors for image-based robot localization and 3-D reconstruction

Planar catadioptric vision sensors consist of a pinhole camera observing a scene being reflected on two (or more) planar mirrors. These systems have recently received an increasing attention because, unlike stereo cameras, can capture two views of the same scene without the need of hardware multi-camera synchronization and calibration. In this paper we explore the original scenario in which a robot manipulator, equipped with a pinhole camera on its end-effector, observes an unknown 3-D scene both directly and reflected through multiple mirrors. We present new multiple-view properties for this scenario and, based on these theoretical results, we present new image-based camera localization and new 3-D scene reconstruction algorithms. Extensive simulation and real-data experiments illustrate the theory and show the effectiveness of the proposed designs.     

3-D model localization using high-resolution reconstruction ofmonocular image sequences

In this paper, we present a complete system for the recognition and localization of a three-dimensional (3-D) model from a sequence of monocular images with known motion. The originality of this system is twofold. First, it uses a purely 3-D approach, starting from the 3-D reconstruction of the scene and ending by the 3-D matching of the model. Second, unlike most monocular systems, we do not use token tracking to match successive images. Rather, subpixel contour matching is used to recover more precisely complete 3-D contours. In contrast with the token tracking approaches, which yield a representation of the 3-D scene based on disconnected segments or points, this approach provides us with a denser and higher level representation of the scene. The reconstructed contours are fused along successive images using a simple result derived from the Kalman filter theory. The fusion process increases the localization precision and the robustness of the 3-D reconstruction. Finally, corners are extracted from the 3-D contours. They are used to generate hypotheses of the model position, using a hypothesize-and-verify algorithm that is described in detail. This algorithm yields a robust recognition and precise localization of the model in the scene. Results are presented on infrared image sequences with different resolutions, demonstrating the precision of the localization as well as the robustness and the low computational complexity of the algorithms.     

Interior-point methodology for 3-D PET reconstruction

Interior-point methods have been successfully applied to a wide variety of linear and nonlinear programming applications. This paper presents a class of algorithms, based on path-following interior-point methodology, for performing regularized maximum-likelihood (ML) reconstructions on three-dimensional (3-D) emission tomography data. The algorithms solve a sequence of subproblems that converge to the regularized maximum likelihood solution from the interior of the feasible region (the nonnegative orthant). We propose two methods, a primal method which updates only the primal image variables and a primal-dual method which simultaneously updates the primal variables and the Lagrange multipliers. A parallel implementation permits the interior-point methods to scale to very large reconstruction problems. Termination is based on well-defined convergence measures, namely, the Karush-Kuhn-Tucker first-order necessary conditions for optimality. We demonstrate the rapid convergence of the path-following interior-point methods using both data from a small animal scanner and Monte Carlo simulated data. The proposed methods can readily be applied to solve the regularized, weighted least squares reconstruction problem.     

Fully 3-D PET reconstruction with system matrix derived from point source measurements

The quality of images reconstructed by statistical iterative methods depends on an accurate model of the relationship between image space and projection space through the system matrix The elements of the system matrix for the clinical Hi-Rez scanner were derived by processing the data measured for a point source at different positions in a portion of the field of view. These measured data included axial compression and azimuthal interleaving of adjacent projections. Measured data were corrected for crystal and geometrical efficiency. Then, a whole system matrix was derived by processing the responses in projection space. Such responses included both geometrical and detection physics components of the system matrix. The response was parameterized to correct for point source location and to smooth for projection noise. The model also accounts for axial compression (span) used on the scanner. The forward projector for iterative reconstruction was constructed using the estimated response parameters. This paper extends our previous work to fully three-dimensional. Experimental data were used to compare images reconstructed by the standard iterative reconstruction software and the one modeling the response function. The results showed that the modeling of the response function improves both spatial resolution and noise properties.     

基于二阶统计量的四维近场源定位

本文针对近场窄带信源频率、仰角、方向角和距离四维参数的估计问题，提出了一种基于二阶统计量的近场源四维参数估计方法。该方法利用中心对称的十字阵列和多重旋转矢量不变技术估计信源参数，无需谱峰搜索，同时避免应用四阶累积量，减轻了算法的计算量。仿真实验证明了该算法的有效性。     

A neural-learning-based reflectance model for 3-D shape reconstruction

In this letter, the limitation of the conventional Lambertian reflectance model is addressed and a new neural-based reflectance model is proposed of which the physical parameters of the reflectivity under different lighting conditions are interpreted by the neural network behavior of the nonlinear input-output mapping. The idea of this method is to optimize a proper reflectance model by a neural learning algorithm and to recover the object surface by a simple shape-from-shading (SFS) variational method with this neural-based model. A unified computational scheme is proposed to yield the best SFS solution. This SFS technique has become more robust for most objects, even when the lighting conditions are uncertain.     

Optimal CT scanning plan for long-bone 3-D reconstruction

Digital computed tomographic (CT) data are widely used in three-dimensional (3-D) construction of bone geometry and density features for 3-D modelling purposes. During in vivo CT data acquisition the number of scans must be limited in order to protect patients from the risks related to X-ray absorption. The aim of this work is to automatically define, given a finite number of CT slices, the scanning plan which returns the optimal 3-D reconstruction of a bone segment from in vivo acquired CT images. An optimization algorithm based on a Discard-Insert-Exchange technique has been developed. In the proposed method the optimal scanning sequence is searched by minimizing the overall reconstruction error of a two-dimensional (2-D) prescanning image: an anterior-posterior (AP) X-ray projection of the bone segment. This approach has been validated in vitro on 3 different femurs. The 3-D reconstruction errors obtained through the optimization of the scanning plan on the 3-D prescanning images and on the corresponding 3-D data sets have been compared. 2-D and 3-D data sets have been reconstructed by linear interpolation along the longitudinal axis. Results show that direct 3-D optimization yields root mean square reconstruction errors which are only 4%-7% lower than the 2-D-optimized plan, thus proving that 2-D-optimization provides a good suboptimal scanning plan for 3-D reconstruction. Further on, 3-D reconstruction errors given by the optimized scanning plan and a standard radiological protocol for long bones have been compared. Results show that the optimized plan yields 20%-50% lower 3-D reconstruction errors     

3-D reconstruction of 2-D crystals in real space

A new algorithm for three-dimensional reconstruction of two-dimensional crystals from projections is presented, and its applicability to biological macromolecules imaged using transmission electron microscopy (TEM) is investigated. Its main departures from the traditional approach is that it works in real space, rather than in Fourier space, and it is iterative. This has the advantage of making it convenient to introduce additional constraints (such as the support of the function to be reconstructed, which may be known from alternative measurements) and has the potential of more accurately modeling the TEM image formation process. Phantom experiments indicate the superiority of the new approach even without the introduction of constraints in addition to the projection data.     

Contour reconstruction in 3-D X-ray CT

The authors derives an algorithm for reconstructing contours of an object from 3D cone beam X-ray data. By reducing the amount of the searched-for information, contours, or density jumps instead of the densities themselves, the authors are able to develop fast algorithms for data incomplete with respect to both the movement of the X-ray source and the detector reading. The method is related to local or Lambda tomography. Numerical simulations show the efficiency of the algorithm.     

Computing reconstruction kernels for circular 3-D cone beam tomography

In this paper, we present techniques for deriving inversion algorithms in 3-D computer tomography. To this end, we introduce the mathematical model and apply a general strategy, the so-called approximate inverse, for deriving both exact and numerical inversion formulas. Using further approximations, we derive a 2-D shift-invariant filter for circular-orbit cone-beam imaging. Results from real data are presented.      

A 3-D SAR model for current source interstitial hyperthermia

A three-dimensional (3-D) model is presented for the calculation of the specific absorption rate (SAR) in human tissue during current source interstitial hyperthermia. The model is capable of millimeter resolution and can cope with irregular implants in heterogeneous tissue. The SAR distribution is calculated from the electrical potential. The potential distribution is determined by the dielectric properties of the tissue and by the electrode configuration. The dielectric properties and the current injection of the electrodes are represented on a 3-D uniform grid. The calculated potential at an electrode current injection point is not the actual electrode potential at that point. To estimate this potential a grid independent representation of an electrode together with an analytical solution in the neighborhood of the electrode are used. The calculated potential on the electrode surface is used to estimate the electrode impedance. The tissue implementation is validated by comparing calculated distributions with analytical solutions. The electrode implementation is verified by comparing different discretizations of an electrode configuration and by comparing numerically calculated electrode impedances with analytically calculated impedances     

一种用于人脸识别的三维人脸模型重建方法

针对以往三维人脸模型重建算法实用性差、算法复杂度高、需要通用人脸模型和对噪声敏感等缺陷，提出了一种计算量小、无需通用人脸模型的三维人脸模型的重建算法。该算法在人工辅助确定特征点的基础上，利用能量函数最小的约束关系实现深度图的初步融合，然后运用改进ICP算法获得隐式的三维人脸模型。通过对获得模型的变换和投影，可产生不同姿态的二维人脸图像。实验结果表明，融合平均误差仅为1．32毫米，效果逼真。和其它算法相比，它还具有存储资源消耗少、算法稳定性高等优点。     

Resolution and noise properties of MAP reconstruction for fully 3-D PET

We derive approximate analytical expressions for the local impulse response and covariance of images reconstructed from fully three-dimensional (3-D) positron emission tomography (PET) data using maximum a posteriori (MAP) estimation. These expressions explicitly account for the spatially variant detector response and sensitivity of a 3-D tomograph. The resulting spatially variant impulse response and covariance are computed using 3-D Fourier transforms. A truncated Gaussian distribution is used to account for the effect on the variance of the nonnegativity constraint used in MAP reconstruction. Using Monte Carlo simulations and phantom data from the microPET small animal scanner, we show that the approximations provide reasonably accurate estimates of contrast recovery and covariance of MAP reconstruction for priors with quadratic energy functions. We also describe how these analytical results can be used to achieve near-uniform contrast recovery throughout the reconstructed volume.     

基于开源软件的电信业务平台重构设计和分析

业务平台重构是中国电信网络智能化重构举措的重要组成部分,分析了业务平台存在的问题,给出业务平台重构的要求、架构和开源实现,以管理平台为案例给出重构的路径方法,从开源思维、技术研究、人才建设角度提出重构保障要求.     

A computer-assisted system for 3-D frameless localization in stereotaxic MRI

A low-cost PC-based system for 3-D localization of brain targets in stereotaxic imaging is presented. It relies on a method, using MR images, in which four markers are inserted in the fastenings of a Talairach stereotaxic frame during MRI examination. By locating these markers on the images with this system, the transformation matrixes can be computed to obtain the 3-D coordinates of the center of a tumour in the stereotaxic space or in the MRI space. The system calculates the frame and arc setting parameters of a probe trajectory to the target, either for an orthogonal or a double oblique approach if needed. Simulated probe trajectory intersections with consecutive slices can be viewed in order to validate the trajectory before and during the surgical procedure. The method presents no major constraints in routine examinations. Mathematical details on the calculation of the transformation matrices are given.     

Towards 3-D scene reconstruction from broadcast video

Three-dimensional (3-D) scene reconstruction from broadcast video is a challenging problem with many potential applications, such as 3-D TV, free-view TV, augmented reality or three-dimensionalization of two-dimensional (2-D) media archives. In this paper, a flexible and effective system capable of efficiently reconstructing 3-D scenes from broadcast video is proposed, with the assumption that there is relative motion between camera and scene/objects. The system requires no a priori information and input, other than the video sequence itself, and capable of estimating the internal and external camera parameters and performing a 3-D motion-based segmentation, as well as computing a dense depth field. The system also serves as a showcase to present some novel approaches for moving object segmentation, sparse and dense reconstruction problems. According to the simulations for both synthetic and real data, the system achieves a promising performance for typical TV content, indicating that it is a significant step towards the 3-D reconstruction of scenes from broadcast video.     

A survey of motion-parallax-based 3-D reconstruction algorithms

The task of recovering three-dimensional (3-D) geometry from two-dimensional views of a scene is called 3-D reconstruction. It is an extremely active research area in computer vision. There is a large body of 3-D reconstruction algorithms available in the literature. These algorithms are often designed to provide different tradeoffs between speed, accuracy, and practicality. In addition, even the output of various algorithms can be quite different. For example, some algorithms only produce a sparse 3-D reconstruction while others are able to output a dense reconstruction. The selection of the appropriate 3-D reconstruction algorithm relies heavily on the intended application as well as the available resources. The goal of this paper is to review some of the commonly used motion-parallax-based 3-D reconstruction techniques and make clear the assumptions under which they are designed. To do so efficiently, we classify the reviewed reconstruction algorithms into two large categories depending on whether a prior calibration of the camera is required. Under each category, related algorithms are further grouped according to the common properties they share.     

Parallelization of the EM algorithm for 3-D PET image reconstruction

The EM algorithm for PET image reconstruction has two major drawbacks that have impeded the routine use of the EM algorithm: the long computation time due to slow convergence and a large memory required for the image, projection, and probability matrix. An attempt is made to solve these two problems by parallelizing the EM algorithm on multiprocessor systems. An efficient data and task partitioning scheme, called partition-by-box, based on the message passing model is proposed. The partition-by-box scheme and its modified version have been implemented on a message passing system, Intel iPSC/2, and a shared memory system, BBN Butterfly GP1000. The implementation results show that, for the partition-by-box scheme, a message passing system of complete binary tree interconnection with fixed connectivity of three at each node can have similar performance to that with the hypercube topology, which has a connectivity of log(2) N for N PEs. It is shown that the EM algorithm can be efficiently parallelized using the (modified) partition-by-box scheme with the message passing model.