Camera Lens Distortion Research Articles

CCD Image Geometrical Distortion Correct and Large Touch Screen

There are geometric distortions in all the images produced by the CCD camera lens, and the distortions are all nonlinear. Coordinate conversion is an effective method for geometric correction. The calculations will boost as the higher degrees of the polynomial. It is impossible to apply directly in real time correction system. This article introduces a correction according to divided areas through linear polynomial on the base of higher degree polynomial coordinate conversions. In the method the image is divided into uneven loop like areas at first. In each area you can take a linear polynomial approximating higher degree polynomial. Because CCD camera lens distortions are radial distortions, high precision can be acquired through the linear polynomial on the base of higher degree polynomial. In the same time the calculations can be decreased and meet the needs of real-time correction. Apply this method on large touch screen, it can correct the distortion effectively and is useful in engineering.

A New Model of Bundle Adjustment Used in Camera Calibration

The calibration of the internal and external parameters of each camera is a crucial step for free-form surface measurement based on stereo vision. The nonlinear optimization method of bundle adjustment is usually used in the camera calibration. The model of bundle adjustment can be derived from the nonlinear collinearity equations which contains the nonlinear distortions of camera lens. As the internal parameters of camera, the distortion parameters are implied in the projective transformation matrix, and difficult to be explicitly expressed by an equation. But the establishment of the model of bundle adjustment needs to calculate the first-order partial derivative of each parameter. At present, there is no effective method to decompose the implicit parameters which are taken as a whole in the expansion of first-order partial derivative. The existing bundle adjustment models are not comprehensive and easy to cause ill-conditioned matrix problems. In this paper, we discuss the methods of the nonlinear camera lens distortion correction, establish a new lens distortion expression, and propose a new model of bundle adjustment based on the new distortion expression. Experiments results indicate that the new bundle adjustment model can obtain high camera calibration accuracy, and avoid the ill-conditioned matrix problems.

Stereo depth estimation under different camera calibration and alignment errors

Depth estimation is a fundamental issue in computational stereo. To obtain accurate stereo depth estimation, all mechanical parameters with a high precision need to be measured in order to achieve subpixel accuracy and to match features between two different images. This paper investigates accurate depth estimation with different mechanical parameter errors, such as camera calibration and alignment errors, which mainly result from camera lens distortion, camera translation, rotation, pitch, and yaw. For each source of the errors, a model for the error description is presented, and the accurate depth estimation due to this error is quantitatively analyzed. Depth estimation algorithms under an individual error, and with all the errors, are given. Experimental results show that the proposed models can rectify the errors and calculate the accurate depths effectively.

Using the camera pin-hole model restrictions to calibrate the lens distortion model

Camera calibration required the computation of camera pin-hole and lens distortion models. The lens distortion is estimated alone or together with the pin-hole model, by using some existing lens distortion non-metric or self-calibration methods. If both models are computed together, then the models are adjusted to training data, but not to real camera. This is because both pin-hole and lens distortion models are coupled. If they are computed separately, difficulties arise since calibration of lens distortion alone is an unstable process. To improve existing camera calibration methods, this paper proposes a metric calibration method to compute lens distortion separately from the pin-hole model. This method is solved under stable conditions, independently of the computed lens distortion model, since pin-hole and distortion models are computed separately. Images of a planar template are used. First, using distorted control points extracted from images, a set of undistorted points which fits in the pin-hole model are computed. Second, with distorted and undistorted control points, lens distortion is calibrated by using a metric calibration process.

Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform

Before an aerial image can be used to support a site-specific application it is essential to perform the geometric corrections and geocoding. This research discusses the development of an automatic aerial image georeferencing method for an unmanned aerial vehicle (UAV) image data acquisition platform that does not require use of ground control points (GCP). An onboard navigation system is capable of providing continuous estimates of the position and attitude of the UAV. Based on a navigation data and a camera lens distortion model, the image collected by an onboard multispectral camera can be automatically georeferenced. When compared with 16 presurveyed ground reference points, image automatic georeferenced results indicated that position errors were less than 90 cm. A large field mosaic image can be generated according to the individual image georeferenced information. A 56.9 cm mosaic error was achieved. This accuracy is considered sufficient for most of the intended precision agriculture applications.

Using horizontal and vertical building structure to constrain indirect sensor orientation

This paper presents a method to integrate linear horizontal, vertical and right-angled scene structures into the bundle adjustment of image sequences. An increasing number of airborne image acquisition systems is available and equipped with non-metric small- or medium-frame cameras and no or insufficiently accurate INS devices. In cases where the data is to be used for the production of geo-spatial data, where a certain accuracy and precision is required, an indirect sensor orientation, possibly including self-calibration, needs to be performed. The idea which led to the presented approach is to reduce the number of GCPs necessary for this task by applying the mentioned scene structures. The method directly uses the linear structures, visible at man-made objects as fictive observations within the adjustment, while self-calibration of intrinsic camera parameters and lens distortion is included as well. Experiments with two datasets demonstrate that, through this method, only limited GCP information is required to obtain satisfactory results. In fact, in one experiment using oblique images, several scene constraints were provided and only the datum was defined by ground control. The residuals at check points from this setup were similar to the traditional case where several well-distributed GCPs were available in the scene. In the second experiment the ability of this approach to support the bundle adjustment was shown for a UAV dataset. Although no GCP and camera calibration information was available, the visual inspection of adjusted object points and the residuals at horizontal structures confirmed the ability of the method to align an image block with the structure, as embodied in the defined scene constraints. Despite the convincing outcome of the experiments, it needs to be mentioned that some manual work is still involved in defining the constraints. In future work the issue of automation will be addressed.

Assessment of Erosion, Deposition and Rill Development On Irregular Soil Surfaces Using Close Range Digital Photogrammetry

Abstract Understanding the spatial pattern of erosion in inter‐rill and rill areas can be useful in locating sources of sediment and in determining down‐slope sediment delivery. However, spatial information about erosion processes in the inter‐rill and rill areas under field conditions is often limited owing to the non‐availability of a detailed microtopography. This paper aims to present a method for the survey of microtopographic soil surface changes caused by erosion and to investigate the development of sediment transport and rill erosion in the inter‐rill and rill areas for a given rainfall event. The approach is based on the application of close range digital photogrammetry to produce differences of digital elevation models (DEMs), automatically measured and generated but with manual 3D point editing. Quality analysis of the DEM measurements has shown that erosion and deposition can be detected with a relative error of ±2·8 to ±5·3 mm. The minimum average sediment yield from plots with an area of 53 m 2 after the occurrence of 46 to 70 mm of erosive rainfall was estimated to be 1·24 to 3·5 kg m −2 . The spatial pattern of erosion and deposition on inter‐rill and rill areas has proved the relative efficiency of rills to transport sediments from inter‐rill areas with a sediment delivery ratio greater than one. Thus at erosive rainfall events, the transport capacity of the rill flow was not a limiting condition. The rill network was controlled by abrupt change in surface microtopography where rough surfaces produced many smaller rills but fewer contributing rills than relatively smooth surfaces. Tillage‐induced surface roughness, the presence of stones and sparse vegetation, and the borders of individual plots were found to present challenges to the photogrammetric techniques, and to introduce errors which inhibit fully automatic DEM generation. The precision of the DEMs was constrained by the precision of ground control points (GCPs), camera lens distortion towards format edges and DEM extraction parameters. Despite this limitation, however, the application of digital photogrammetry enabled the measurement of high‐frequency microtopographic surfaces and characterisation of the spatial distribution of erosion and deposition and of rill erosion development during highly erosive rainfall events.

Least-squares calibration method for fringe projection profilometry considering camera lens distortion

By using the least-squares fitting approach, the calibration procedure for fringe projection profilometry becomes more flexible and easier, since neither the measurement of system geometric parameters nor precise control of plane moving is required. With consideration of camera lens distortion, we propose a modified least-squares calibration method for fringe projection profilometry. In this method, camera lens distortion is involved in the mathematical description of the system for least-squares fitting to reduce its influence. Both simulation and experimental results are shown to verify the validity and ease of use of this modified calibration method.

基于共面点直接线性变换的摄像机畸变校正

基于共面点直接线性变换的摄像机畸变校正

Correcting non-linear lens distortion in cameras without using a model

Camera lens distortion calibration is the first step in resolving any metric application with a camera. To date, lens distortion was corrected using some existing lens distortion non-metric or self-calibration methods. Using a lens distortion model means defining a global rule to correct the entire image. This global rule does not take into account particular lens distortion effects not represented by the model. Moreover, to calibrate the model, only some features of the scene such as straight lines, circles or vanishing points are used. Since only the feature of the scene used to calibrate the model is guaranteed by the distortion rectification, it is certain that the model will not be precise. The result is an approximation of the real image distortion. To improve the lens distortion rectification, a method without using a model is proposed. Using a set of control points distributed across the entire image, they are corrected to assure all the restrictions of the scene. With both sets of points, the points detected in the image and the undistorted ones, image local transformations are defined considering only nearby control points. Rather than calibrating a global model, local functions are characterized. The distortion correction is defined by a rectification surface composed of local surface patches each influenced by nearby control points. This method is more sensitive to local deformations and allows the image to be corrected in accordance with its distortion.

Robust metric calibration of non-linear camera lens distortion

Camera lens distortion is crucial to obtain the best performance cameral model. Up to now, different techniques exist, which try to minimize the calibration error using different lens distortion models or computing them in different ways. Some compute lens distortion camera parameters in the camera calibration process together with the intrinsic and extrinsic ones. Others isolate the lens distortion calibration without using any template and basing the calibration on the deformation in the image of some features of the objects in the scene, like straight lines or circles. These lens distortion techniques which do not use any calibration template can be unstable if a complete camera lens distortion model is computed. They are named non-metric calibration or self-calibration methods. Traditionally a camera has been always best calibrated if metric calibration is done instead of self-calibration. This paper proposes a metric calibration technique which computes the camera lens distortion isolated from the camera calibration process under stable conditions, independently of the computed lens distortion model or the number of parameters. To make it easier to resolve, this metric technique uses the same calibration template that will be used afterwards for the calibration process. Therefore, the best performance of the camera lens distortion calibration process is achieved, which is transferred directly to the camera calibration process.

Computational tasks in computer-assisted transbronchial biopsy

The paper presents algorithmic solutions dedicated to computer navigation system which is to assist bronchoscope positioning during transbronchial needle-aspiration biopsy. The navigation exploits the principle of on-line registration of real images from endoscope camera and the virtual ones generated on the base of computed tomography (CT) data of a patient. When these images are similar an assumption is made, that the bronchoscope and virtual camera have approximately the same position and view direction. In this paper the following computational aspects are described: correction of camera lens distortion, fast approximate estimation of endoscope ego-motion, reconstruction of bronchial tree from the CT data by means of their segmentation and its centerline calculation, virtual views generation, registration of real and virtual images via maximization of their mutual information and, finally, efficient parallel and network implementation of the navigation system which is under development at present.

3D curve structure reconstruction from a sparse set of unordered images

A flexible and portable approach to reconstructing 3D curve structures of free-form objects from a sparse set of unordered images taken by a hand-held CCD camera is presented. To make the whole pipeline robust and accurate, we mark the interested curves on the object to make them visually outstand from the background. Some specifically designed markers are distributed around the object as well. Followed by an automatic camera pose recovery and camera lens distortion correction step, a user-friendly semi-automatic method is put forward to extract the curves in the images. The curve correspondences in stereo image pairs are then optimally established and the 3D curve structures are recovered by using the stereo triangulation method. Experiments show that the method is applicable for reverse modeling of freeform objects with moderate accuracy requirements.

摄像机镜头畸变的一种非量测校正方法

摄像机镜头畸变的一种非量测校正方法

A new calibration model of camera lens distortion

Lens distortion is one of the main factors affecting camera calibration. In this paper, a new model of camera lens distortion is presented, according to which lens distortion is governed by the coefficients of radial distortion and a transform from ideal image plane to real sensor array plane. The transform is determined by two angular parameters describing the pose of the real sensor array plane with respect to the ideal image plane and two linear parameters locating the real sensor array with respect to the optical axis. Experiments show that the new model has about the same correcting effect upon lens distortion as the conventional model including all the radial distortion, decentering distortion and prism distortion. Compared with the conventional model, the new model has fewer parameters to be calibrated and more explicit physical meaning.

A PDE Method to Segment Image Linear Objects with Application to Lens Distortion Removal

In this paper, we propose a partial differential equation based method to segment image objects, which have a given parametric shape based on energy functional. The energy functional is composed of a term that detects object boundaries and a term that constrains the contour to find a shape compatible with the parametric shape. While the shape constraints guiding the PDE may be determined from object\'s shape statistical models, we demonstrate the proposed approach on the extraction of objects with explicit shape parameterization, such as linear image segments. Several experiments are reported on synthetic and real images to evaluate our approach. We also demonstrate the successful application of the proposed method to the problem of removing camera lens distortion, which can be significant in medium to wide-angle lenses.

Nonmetric calibration of camera lens distortion: differential methods and robust estimation

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive new distortion measures that can be optimized using nonlinear search techniques to find the best distortion parameters that straighten these lines. Unlike the other existing approaches, we also provide fast, closed-form solutions to the distortion coefficients. We prove that including both the distortion center and the decentering coefficients in the nonlinear optimization step may lead to instability of the estimation algorithm. Our approach provides a way to get around this, and, at the same time, it reduces the search space of the calibration problem without sacrificing the accuracy and produces more stable and noise-robust results. In addition, while almost all existing nonmetric distortion calibration methods needs user involvement in one form or another, we present a robust approach to distortion calibration based on the least-median-of-squares estimator. Our approach is, thus, able to proceed in a fully automatic manner while being less sensitive to erroneous input data such as image curves that are mistakenly considered projections of three-dimensional linear segments. Experiments to evaluate the performance of this approach on synthetic and real data are reported.

What you see if what you get

A self-calibration method is presented for self-calibrating camera lens distortion by using only the image correspondences of two views. Two images of a single object are related by the epipolar geometry, which can be described by a 3 /spl times/ 3 singular matrix called fundamental matrix. It captures all geometric information contained in two images. An optimization method is applied to minimize the epipolar distances of the two images by adjusting the camera lens radial distortion coefficient. The merit of the method is that it does not rely on any ground truth data. Simulation and experimental results are given to demonstrate the applicability of the method.

Auto-calibration of an SMT machine by machine vision

An SMT machine has many working coordinate frames—the fiducial mark camera frame, component camera frame, machine table frame, PCB frame, and reference frame. Because of many influences such as mechanical dimension errors, machine assembling errors, and camera lens distortions, all frames on the SMT machine must be calibrated to compensate for these machine errors. This paper applies machine vision techniques to auto-calibrate an SMT machine, including frame transformations and nozzle compensation, bringing the accuracy of this system to within ±0.1 mm. The coordinate mapping from the camera frame to machine table frame is a quadratic form, while the other frame mappings use linear forms. The merits for this machine’s vision-based, auto-calibration methods are: (1) It has little calibration time, (2) It does not need expensive calibration instruments, and (3) Its expense is very low.

Robust and efficient vision system for group of cooperating mobile robots with application to soccer robots

In this paper a global vision scheme for estimation of positions and orientations of mobile robots is presented. It is applied to robot soccer application which is a fast dynamic game and therefore needs an efficient and robust vision system implemented. General applicability of the vision system can be found in other robot applications such as mobile transport robots in production, warehouses, attendant robots, fast vision tracking of targets of interest and entertainment robotics. Basic operation of the vision system is divided into two steps. In the first, the incoming image is scanned and pixels are classified into a finite number of classes. At the same time, a segmentation algorithm is used to find corresponding regions belonging to one of the classes. In the second step, all the regions are examined. Selection of the ones that are a part of the observed object is made by means of simple logic procedures. The novelty is focused on optimization of the processing time needed to finish the estimation of possible object positions. Better results of the vision system are achieved by implementing camera calibration and shading correction algorithm. The former corrects camera lens distortion, while the latter increases robustness to irregular illumination conditions.