Simple Camera Calibration Research Articles

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision.

At present, most of the handling industrial robots working on the production line are operated by teaching or preprogramming, which makes the flexibility of the production line poor and does not meet the flexible production requirements of the material handling system. This study proposes a solution based on adding computer binocular vision to a five-axis industrial robot system. A simple and high-precision binocular camera calibration method is proposed, the kinematics of the five-axis robot is analyzed, and the target positioning is realized; the communication between the upper and lower robots is realized through Ethernet. According to the specific target, the grasping scheme of the gripper was designed; the control software was developed using two schemes. Visual control is carried out by operating specific buttons on the control panel, and visual control is carried out by executing the macrovariable program, finally realizing the joint fusion of multisensor data and binocular vision.

Simple and precise multi-view camera calibration for 3D reconstruction

A precise calibration in multi-view camera environments allows to perform accurate 3D object reconstruction, precise tracking of objects and accurate pose estimation. Those techniques are of high value in the industry today in fields as quality control or automation. In the present work, an improvement of a simple existing multi-view camera calibration method is presented. The improved method employs a specially developed reference token to overcome some issues in the original algorithm. We prove that the new method overcomes those problems thus attaining a higher accuracy while keeping the process simple and the implementation costs low. This last aspects makes the method interesting for the industry but specially suitable for SMEs typical in traditional sectors.

Une interface pour utilisateur de caméra 3D employée pour enregistrer les angles des poignets lors de prestations au piano

Injuries are common over a performing musician’s career and wrist injuries are the most frequent site of pain for pianists. Although general recommendations insist on keeping wrists in a “neutral” position to avoid injury, this is rarely done in practice. Recent advances in motion capture technology may aid in raising students’ awareness of the propensity to use wrist positions outside of the recommended “neutral.” These technologies may be used to measure precise wrist positions in piano playing in order to set specific thresholds for avoiding injury. This paper discusses various advantages and limitations of motion capture technologies, including data visualization and usage within the music instrument pedagogy framework in order to define a set of requirements for an accessible motion-tracking system. A prototype of a dedicated image-processing-based system with a graphical user interface that meets these requirements is described. This system uses passive coloured markers and a standard 3D camera, encouraging use outside the traditional laboratory environment. Simple camera calibration options and basic hand tracking from aerial view images allow monitoring of wrist flexion/extension over short video recordings. Measurements are compared to flexion/extension thresholds recommended for typists to prevent carpal tunnel pressure, and moments of approaching or exceeding these thresholds are flagged to the user both in real time and in post-performance. Potential applications include monitoring the practice of short technical passages without restriction of instrument or location.

THE EFFECT OF VARIES CAMERA CALIBRATION FIELDS ON CAMERA PARAMETERS

All photogrammetric applications need good camera parameters for mapping purpose, such as an Unmanned Aerial Vehicle (UAV) that encompassed with camera devices. Simple camera calibration is commonly used in many experiments in order to obtain the camera parameter’s value. In aerial mapping, interior camera parameters value of close-range camera calibration is used to correct the image error. However, the changes of the interior calibration parameters used need to be considered at different heights of mapping. Therefore, this research aims to contribute by analyzing of camera parameters’ changes from three heights using calibration field, and one camera calibration in the laboratory as commonly used. Camera distance heights of 15 metre, 25 metre, 55 metre, and 1.4 metre camera distance in the laboratory. The results show the changes in camera parameter’s value. Hence, value of camera calibration parameters of a camera is considered different and can change depend on the distance (height) of calibration.

A simplified nonlinear regression method for human height estimation in video surveillance

This paper presents a simple camera calibration method for estimating human height in video surveillance. Given that most cameras for video surveillance are installed in high positions at a slightly tilted angle, it is possible to retain only three calibration parameters in the original camera model, namely the focal length, the tilting angle and the camera height. These parameters can be directly estimated using a nonlinear regression model from the observed head and foot points of a walking human instead of estimating the vanishing line and point in the image, which is extremely sensitive to noise in practice. With only three unknown parameters, the nonlinear regression model can fit data efficiently. The experimental results show that the proposed method can predict the human height with a mean absolute error of only about 1.39 cm from ground truth data.

Camera calibration by multiplexed phase encoding of coordinate information.

A simple camera calibration method based on the principle of phase encoding and coordinate transformation is proposed. We use a reference coordinate frame encoded as a phase distribution by multiplexing the x and y directions. From this, we suggest a phase demodulation system. The coordinate transformation induced by the imaging is exploited to estimate the intrinsic and extrinsic camera parameters by using the least-squares method. Thus, a robust and noniterative estimation scheme is obtained. Simulations and experimental results show the feasibility of the proposal. Because of the potential for calibrating projectors, the proposed method could be used to calibrate fringe-projection systems.

Analysis of Camera Parameters Value in Various Object Distances Calibration

In photogrammetric applications, good camera parameters are needed for mapping purpose such as an Unmanned Aerial Vehicle (UAV) that encompassed with non-metric camera devices. Simple camera calibration was being a common application in many laboratory works in order to get the camera parameter's value. In aerial mapping, interior camera parameters' value from close-range camera calibration is used to correct the image error. However, the causes and effects of the calibration steps used to get accurate mapping need to be analyze. Therefore, this research aims to contribute an analysis of camera parameters from portable calibration frame of 1.5 × 1 meter dimension size. Object distances of two, three, four, five, and six meters are the research focus. Results are analyzed to find out the changes in image and camera parameters' value. Hence, camera calibration parameter's of a camera is consider different depend on type of calibration parameters and object distances.

The On-Line Calibration Research of the Picking Robot Binocular Vision System

In the picking robot binocular vision systems research, the camera calibration is often an indispensable step and these basements to locate the target of the object and rebuild the three-dimensional construction based on the robot stereo vision for the follow-up study. So, searching for a high accuracy and simple camera calibration algorithm is of great significance and necessary. However, For most of these camera calibration algorithms, it is necessary to establish a reference object, namely the target, in front of the camera at present, but posing the target is very not convenient or almost impossible in some cases. Therefore, a picking robot online calibration algorithm based on the vision scene was proposed by studying the work environment characteristics of the picking robot binocular vision system and the invariant projective geometry. The experimental results showed that this algorithm’s calibration accuracy and precision good meets to the requirement of the robot binocular vision system camera calibration in the complex environment.

Simple Camera Calibration From a Single Image Using Five Points on Two Orthogonal 1-D Objects

We propose a simple and practical calibration technique that effectively estimates camera parameters from just five points on two orthogonal 1-D objects, each which has three collinear points, one of which is shared. We derive the basic equations needed to realize camera calibration from just five points observed on a single image that captures the objects. We describe a new camera calibration algorithm that estimates the camera parameters based on the basic equations and optimizes them by the bundle adjustment technique. Our method is validated by both computer simulated data and real images. The results show that the camera parameters yielded by our method are close to those yielded by existing methods. The tests demonstrate that our method is both effective and practical.

A study on three-dimensional vision system for machining setup verification

In computerized numerical control (CNC) machine tools, it is often a time-consuming and error-prone process to verify the Euclidean position accordance between the actual machining setup and its designed three-dimensional (3D) digital model. The model mainly contains the work piece and jigs. The mismatch between them will cause a failure of simulation to precisely detect the collision. The paper presents an on-machine 3D vision system to quickly verify the similarity between the actual setup and its digital model by real and virtual image processing. In this paper, the system is proposed first. Afterwards, a simple on-machine camera calibration process is presented. This calibration process determines all the camera's parameters with respect to the machine tool's coordinate frame. The accurate camera mathematical model (or virtual camera) is derived according to the actual imaging projection. Both camera-captured real images and system-generated virtual images are compensated to make them theoretically and practically identical. The mathematical equations have been derived. Using the virtual image as a reference and then superimposing the real image onto it, the operator can intuitively verify the Euclidean position in accordance to the actual setup and its 3D digital model.

Polarization measurements using a commercial off-the-shelf digital camera

This paper presents a methodology to obtain polarization profiles using a commercial off-the-shelf (COTS) digital camera. It contains calibration techniques to ensure robust and accurate determination of the Stokes parameters and the system polarization state. It calculates the Stokes parameters for each scene pixel along with the degree of polarization, azimuth, and ellipticity angles for each of the RGB channels. A detailed calculation shows that the quasimonochromatic approximation for the wavelength dependence of the quarterwave plate can be extended to include the broadband RGB channels. A simple and inexpensive camera calibration converts RGB values to optical density and then to relative intensity values. An analysis is presented for the wavelength-dependent response of the tuned zero-order quartz quarterwave retarder, the broadband RGB channels, and the fourth Stokes parameter. The noise characteristics of the digital camera are analyzed along with a detailed calibration of the imagery for target characterization applications. An extensive validation process, using white light, compares calculated theoretical results with the empirical results from the COTS camera. A RGB pseudocoloring scheme, related to the Poincaré sphere, is introduced for the visualization of polarization parameters of daylight scenes.

Keeping smart, omnidirectional eyes on you

An adaptive panoramic stereo approach for two cooperative mobile platform is presented. There are four key features in the approach: 1) omnidirectional stereovision with an appropriate vertical FOV, and a simple camera calibration method; 2) cooperative mobile platforms for mutual dynamic calibration and best view planning; 3) 3D matching after meaningful object (human subject) extraction; and 4) real-time performance. The integration of omnidirectional vision with mutual awareness and dynamic calibration strategies allows intelligent cooperation between visual agents. This provides an effective way to solve the problems of limited resources, view planning, occlusion, and motion detection of movable robotic platforms. Experiments have shown that this approach is quite promising.

Camera calibration for a mobile robot prototype

Stereovision is an effective technique to use a CCD video camera to determine the 3D position of a target object from two or more simultaneous views of the scene. Camera calibration is a central issue in finding the position of objects in a stereovision system. This is usually carried out by calibrating each camera independently, and then applying a geometric transformation of the external parameters to find the geometry of the stereo setting. After calibration, the distance of various target objects in the scene can be calculated with CCD video cameras, and recovering the 3D structure from 2D images becomes simpler. However, the process of camera calibration is complicated. Based on the ideal pinhole model of a camera, we describe formulas to calculate intrinsic parameters that specify the correct camera characteristics, and extrinsic parameters that describe the spatial relationship between the camera and the world coordinate system. A simple camera calibration method for our CCD video cameras and corresponding experiment results are also given.

Fluid flow visualization by three-dimensionally reconstructed tracer path lines

The development of a measurement system for the visualization, topological classification and quantitative analysis of complex flows in large-scale wind tunnel experiments is described. A new approach is sought whereby the topological features of the flow, e.g. stream lines, separation and reattachment regions, stagnation points and vortex lines are extracted directly and are preferably visualized in real-time in a virtual wind tunnel environment. The system is based on a stereo arrangement of two CCD cameras. A frame rate of 120 f/s allows measurements at high flow velocities. Helium filled soap bubbles are used as tracer particles. The present paper describes a simple camera calibration procedure for large measurement environments and examines the problem of fast and accurate reconstruction of path lines in three dimensions, which will enable true three-dimensional and time-resolved fluid flow visualization. Experimentally obtained visualization results for a free-stream flow, flow around a circular plate and flow over a delta wing are presented.

Using neural networks for 3D measurement in stereo vision inspection systems

This paper presents a stereo vision inspection process which derives precise 3D measurements. Two artificial neural networks are used to facilitate the whole measurement process. At first, a simple camera calibration process is developed to derive the focal lengths and the relative information. A Hopfield neural network is used to solve the stereo matching problem, which has been constructed as an energy function. By means of a recursive process, the disparities of extracted feature points are obtained. In addition, a backpropagation neural network-based measurement error correction model for 3D measurement is proposed. It reduces the errors of 3D measurement associated with a part's orientation, position, magnitude and distance between the object and cameras. Four procedural processes are designed to implement this model. Our laboratory experiments demonstrate that the proposed measurement process has a satisfactory measurement result.

New algorithm for recognizing cylindrical patterns by surface backprojection

In this paper, a simple and fast approach is proposed to recognize the unknown cylindrical patterns using backprojection techniques. The method uses a known-height label pasted on the cylindrical object, on which an object pattern (so-called cylindrical patterns) is associated for the robot to identify the desired object. First, a simple camera calibration method is used to compute the camera parameters by a single camera view of the object. A surface backprojection technique is then adopted to reconstruct the pattern on the cylindrical surface of the object. Finally, the reconstructed pattern is matched with those of each object model using the difference of their NRD spectrum. Experimental results to prove the feasibility of the proposed method are also included.

A non-iterative procedure for rapid and precise camera calibration

A simple, rapid and precise camera calibration method is described by which the position and direction of a camera are calculated without using conventional iterative processes. Error parameters are defined as differences in the values of calculated intrinsic parameters from their fixed ones. Camera parameters are adjusted non-iteratively so that the error parameters become sufficiently small. The rotation matrix orthogonality constraints are completely preserved. Experimental error estimation using an actual mark image shows that the position error is less than 0.1 mm and that the direction error is less than 5 × 10 −4 rad, which correspond to relative errors of 5 × 10 −5 and 1 × 10 −4, respectively. These calibration procedures from the image acquisition to the result output are completed automatically within 6s using a VAX11/780 (1 MIPS)