Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy

In this paper, we present a simple and robust method for self-correction of camera distortion using single images of scenes which contain straight lines. Since the most common distortion can be modelled as radial distortion, we illustrate the method using the Harris radial distortion model, but the method is applicable to any distortion model. The method is based on transforming the edgels of the distorted image to a 1-D angular Hough space, and optimizing the distortion correction parameters which minimize the entropy of the corresponding normalized histogram. Properly corrected imagery will have fewer curved lines, and therefore less spread in Hough space. Since the method does not rely on any image structure beyond the existence of edgels sharing some common orientations and does not use edge fitting, it is applicable to a wide variety of image types. For instance, it can be applied equally well to images of texture with weak but dominant orientations, or images with strong vanishing points. Finally, the method is performed on both synthetic and real data revealing that it is particularly robust to noise.     

Dynamic stereo with self-calibration

An approach for incremental refinement of disparity maps obtained from a dynamic stereo sequence of a static scene is presented. The approach has been implemented using a binocular stereo vision system mounted on a mobile robot. A robust least median of squares based algorithm is given for recovering the camera motion between successive viewpoints, which provides a self-calibration mechanism. The recovered motion is utilized for recursive disparity prediction and refinement using a robust Kalman filter model     

Structured light self-calibration with vanishing points

This paper introduces the vanishing points to self-calibrate a structured light system. The vanishing points permit to automatically remove the projector’s keystone effect and then to self-calibrate the projector–camera system. The calibration object is a simple planar surface such as a white paper. Complex patterns and 3D calibrated objects are not required any more. The technique is compared to classic calibration and validated with experimental results.     

Error analysis of pure rotation-based self-calibration

Self-calibration using pure rotation is a well-known technique and has been shown to be a reliable means for recovering intrinsic camera parameters. However, in practice, it is virtually impossible to ensure that the camera motion for this type of self-calibration is a pure rotation. In this paper, we present an error analysis of recovered intrinsic camera parameters due to the presence of translation. We derived closed-form error expressions for a single pair of images with nondegenerate motion; for multiple rotations for which there are no closed-form solutions, analysis was done through repeated experiments. Among others, we show that translation-independent solutions do exist under certain practical conditions. Our analysis can be used to help choose the least error-prone approach (if multiple approaches exist) for a given set of conditions.     

新型自检脉搏传感器的研制

新型自检脉搏传感器是将人体脉搏波转化为电信号进行测量和分析 ,它对中医的脉象有了一个客观的分辨标准 ,便于揭开脉诊现代科学本质。介绍新型自检脉搏传感器的设计及工艺 ,阐述了该传感器的工作原理 ,最后对测试的结果进行分析。     

Bayesian self-calibration of a moving camera

In this paper, a Bayesian self-calibration approach using sequential importance sampling (SIS) is proposed. Given a set of feature correspondences tracked through an image sequence, the joint posterior distributions of both camera extrinsic and intrinsic parameters as well as the scene structure are approximated by a set of samples and their corresponding weights. The critical motion sequences are explicitly considered in the design of the algorithm. The probability of the existence of the critical motion sequence is inferred from the sample and weight set obtained from the SIS procedure. No initial guess for the calibration parameters is required. The proposed approach has been extensively tested on both synthetic and real image sequences and satisfactory performance has been observed.     

Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene

This work proposes a method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used with fundamental matrices to determine the projection matrices. The present approach is based on the formulation of a nonlinear cost function from the determination of a relationship between two points of the scene and their projections in the image planes. The resolution of this function enables us to estimate the intrinsic parameters of different cameras. The strong point of the present approach is clearly seen in the minimization of the three constraints of a self-calibration system (a pair of images, 3D scene, any camera): The use of a single pair of images provides fewer equations, which minimizes the execution time of the program, the use of a 3D scene reduces the planarity constraints, and the use of any camera eliminates the constraints of cameras having constant parameters. The experiment results on synthetic and real data are presented to demonstrate the performance of the present approach in terms of accuracy, simplicity, stability, and convergence.     

Browsing databases with constraint hierarchies

Electronic markets rely on database technology for efficient search for products, services, customers, and trading partners. Yet, database technology was not designed to build electronic markets, and it often fails to fully support market search activities. Market search often requires browsing and incremental search, attribute and constraint tradeoffs, approximate matches, and incorporation of user and task characteristics into searches. None of these requirements are supported directly by the database technology. Three major extensions to database technology are proposed to effectively support electronic markets. First, incremental search techniques are proposed to allow users to browse databases, and to move from solution to solution by following attribute and constraint tradeoffs. These techniques require systems to have some knowledge of similarity, distance, direction, and tradeoffs. Second, typing and stereotyping techniques are introduced to allow users to relate task and user characteristics to product attributes. These techniques require integration of typing systems with database searches. Third, a comprehensive search strategy is developed that combines incremental search and stereotyping techniques by utilizing product, task, and user ontologies.     

Initialization method for self-calibration using 2-views

Recently, 3D structure recovery through self-calibration of camera has been actively researched. Traditional calibration algorithm requires known 3D coordinates of the control points while self-calibration only requires the corresponding points of images, thus it has more flexibility in real application. In general, self-calibration algorithm results in the nonlinear optimization problem using constraints from the intrinsic parameters of the camera. Thus, it requires initial value for the nonlinear minimization. Traditional approaches get the initial values assuming they have the same intrinsic parameters while they are dealing with the situation where the intrinsic parameters of the camera may change. In this paper, we propose new initialization method using the minimum 2 images. Proposed method is based on the assumption that the least violation of the camera's intrinsic parameter gives more stable initial value. Synthetic and real experiment shows this result.     

基于遗传算法的摄像机自标定方法

针对摄像机自标定方法大多需要求解一个多元高维的非线性方程组,其求解过程非常困难,为此提出一种基于遗传算法的摄像机自标定方法,该方法通过二幅或多幅图像的多个对应匹配特征点,利用二个视图之间的极几何关系,建立某些相关约束,最后得到一个代价函数;然后,通过使用Matlab遗传算法工具箱来求解该函数的最小值,进而一次性求出摄像机所有内、外参数.因为该方法不必用到摄像机的外部信息,所以可推广到序列图像中的变参数情况下的摄像机自标定.实验结果表明,该方法简单、有效、快速,可用于摄像机自标定.     

Improved filtering for the bin-packing with cardinality constraint

Previous research shows that a cardinality reasoning can improve the pruning of the bin-packing constraint. We first introduce a new algorithm, called BPCFlow, that filters both load and cardinality bounds on the bins, using a flow reasoning similar to the Global Cardinality Constraint. Moreover, we detect impossible assignments of items by combining the load and cardinality of the bins, using a method to detect items that are either ”too-big” or ”too-small”. This method is adapted to two previously existing filtering techniques along with BPCFlow, creating three new propagators. We then experiment the four new algorithms on Balanced Academic Curriculum Problem and Tank Allocation Problem instances. BPCFlow is shown to be stronger than previously existing filtering, and more computationally intensive. We show that the new filtering is useful on a small number of hard instances, while being too expensive for general use. Our results show that the introduced ”too-big/too-small” filtering can most of the time drastically reduce the size of the search tree and the computation time. This method is profitable in 88% of the tested instances.     

一种用于14 bit SAR ADC的数字自校准算法

为了实现高精度14bit的逐次逼近型SAR(Successive Approximation)模数转换器ADC,提出一种数字自校准算法。该算法通过切换两种电容阵列的工作状态,得到电容之间的失配误差,并在ADC正常工作时,将得到的电容误差加载到电路中达到在转换过程消除失配的目的。最后对一个失配误差为0.5%的14bit的SARADC系统模型进行参数仿真,结果验证了本数字校准算法的正确性和有效性。     

适用于MEMS振动陀螺仪的相位读出自校准方法

相比于传统陀螺仪,MEMS振动陀螺仪具有体积小、质量轻、功耗低、价格低廉等优势,但是由于其本身结构的局限性和工艺加工水平的限制,其在性能上还处于低精度陀螺仪的行列。目前已有若干MEMS自校准方案可以应用于MEMS振动陀螺仪以提高其精度指标,然而已有的技术存在着只能消除某一种因素对精度的影响、需要高精度转台系统支撑或者校准的灵敏度不高等不足。为解决上述问题,设计并提出了一种基于相位读出的MEMS振动陀螺仪自校准方案,该方案能消除工艺误差、器件老化、外部环境变化对陀螺仪精度的影响,而且该方案不需要高精度转台系统支撑、能够消除绝大部分因素对精度的影响、校准的灵敏度更高。实验仿真结果表明,该方案能实现MEMS振动陀螺仪的自校准,提高测量精度,扩大MEMS振动陀螺仪的适用范围。     

A theory of self-calibration of a moving camera

There is a close connection between the calibration of a single camera and the epipolar transformation obtained when the camera undergoes a displacement. The epipolar transformation imposes two algebraic constraints on the camera calibration. If two epipolar transformations, arising from different camera displacements, are available then the compatible camera calibrations are parameterized by an algebraic curve of genus four. The curve can be represented either by a space curve of degree seven contained in the intersection of two cubic surfaces, or by a curve of degree six in the dual of the image plane. The curve in the dual plane has one singular point of order three and three singular points of order two.If three epipolar transformations are available, then two curves of degree six can be obtained in the dual plane such that one of the real intersections of the two yields the correct camera calibration. The two curves have a common singular point of order three.Experimental results are given to demonstrate the feasibility of camera calibration based on the epipolar transformation. The real intersections of the two dual curves are found by locating the zeros of a function defined on the interval [0, 2]. The intersection yielding the correct camera calibration is picked out by referring back to the three epipolar transformations.     

在PB的数据窗口中使用树视图表现层次型数据

ＰｏｗｅｒＢｕｉｌｄｅｒ是目前比较流行的广为使用的数据库前端开发工具,ＤａｔａＷｉｎｄｏｗ是其提供的最有物色的技术。本文介绍了在ＰｏｗｅｒＢｕｉｌｄｅｒ的ＤａｔａＷｉｎｄｏｗ中使用ＴｒｅｅＶｉｅｗ控件的一种方法。     

分层多目标最优化进化算法

文章用进化算法给出了求解二层字典分层多目标最优化的方法,该算法把求解问题转化为多目标最优化,并研究了这两个问题的解集之间的联系。对多目标最优化定义了一个新的选择算子和适应值函数,这样定义的选择算子和适应值函数结合均匀设计能有效地引导搜索,直接求出问题的解而不用逐层求解。数值模拟表明该方法十分有效。     

Geometric constraint solving with geometric transformation

This paper proposes two algorithms for solving geometric constraint systems. The first algorithm is for constrained systems without loops and has linear complexity. The second algorithm can solve constraint systems with loops. The latter algorithm is of quadratic complexity and is complete for constraint problems about simple polygons. The key to it is to combine the idea of graph based methods for geometric constraint solving and geometric transformations coming from rule-based methods.     

Weight constraint programs with evaluable functions

In the current practice of Answer Set Programming (ASP), evaluable functions are represented as special kinds of relations. This often makes the resulting program unnecessarily large when instantiated over a large domain. The extra constraints needed to enforce the relation as a function also make the logic program less transparent. In this paper, we consider adding evaluable functions to answer set logic programs. The class of logic programs that we consider here is that of weight constraint programs, which are widely used in ASP. We propose an answer set semantics to these extended weight constraint programs and define loop completion to characterize the semantics. Computationally, we provide a translation from loop completions of these programs to instances of the Constraint Satisfaction Problem (CSP) and use the off-the-shelf CSP solvers to compute the answer sets of these programs. A main advantage of this approach is that global constraints implemented in such CSP solvers become available to ASP. The approach also provides a new encoding for CSP problems in the style of weight constraint programs. We have implemented a prototype system based on these results, and our experiments show that this prototype system competes well with the state-of-the-art ASP solvers. In addition, we illustrate the utilities of global constraints in the ASP context.     

Intrinsic and extrinsic active self-calibration of multi-camera systems

We present a method for active self-calibration of multi-camera systems consisting of pan-tilt zoom cameras. The main focus of this work is on extrinsic self-calibration using active camera control. Our novel probabilistic approach avoids multi-image point correspondences as far as possible. This allows an implicit treatment of ambiguities. The relative poses are optimized by actively rotating and zooming each camera pair in a way that significantly simplifies the problem of extracting correct point correspondences. In a final step we calibrate the entire system using a minimal number of relative poses. The selection of relative poses is based on their uncertainty. We exploit active camera control to estimate consistent translation scales for triplets of cameras. This allows us to estimate missing relative poses in the camera triplets. In addition to this active extrinsic self-calibration we present an extended method for the rotational intrinsic self-calibration of a camera that exploits the rotation knowledge provided by the camera’s pan-tilt unit to robustly estimate the intrinsic camera parameters for different zoom steps as well as the rotation between pan-tilt unit and camera. Quantitative experiments on real data demonstrate the robustness and high accuracy of our approach. We achieve a median reprojection error of $0.95$ pixel.