Geolocation using video sensor measurements

In this paper, a new panoramic image generation algorithm is proposed based on more realistic image formation processes. Perspective projection, lens distortion, vignetting and illumination effects are incorporated into the proposed panoramic modeling. Intrinsic and extrinsic camera parameters are estimated by the proposed stable camera parameter estimation algorithm derived from panning camera constraints. This paper shows that accurate panoramic images can be reconstructed based on the proposed camera modeling and parameters estimation. The effectiveness of the proposed algorithm is also shown with several image sequences in terms of reconstruction error from the generated panoramic image.

The process of solving camera intrinsic and extrinsic parameters has been presented by using geometric techniques. In contrast with traditional method, which is a pure algebra process, the geometric technique accomplishes the camera parameters more intuitively and easily. If the perspective projection matrix is given, according to the geometric meaning of perspective projection camera model, the coordinates of optical centre can be directly estimated in the world coordinate system. Then rotation matrix and translation vector can be obtained from the matrix. Finally, we can derive camera intrinsic parameters. During the process of derivation, for any given 3 by 4 matrix, the constraint conditions have been discussed in another way when the matrix could be described as a perspective projection transformation. Although it has the same result as pure algebra progress, it's of great significance to understand the geometric meaning among the intrinsic and extrinsic camera parameters

In this paper we present an easy method for multiple camera calibration with common field of view only from vertical lines. The locations of the vertical lines are known in advance. Compared to other calibration objects, the vertical lines have some good properties, since they can be easily built and can be visible by cameras in any direction simultaneously. Given 5 fixed vertical lines, an image containing them taken by a camera may provide 2 constraints in the intrinsic parameters of the camera, and extrinsic parameters can then be recovered. The calibration procedure consists of three main steps: Firstly, the image is rectified by a homography, which makes the projections of vertical lines parallel to u-axis in the rectified image. Secondly, for any vertical scan line in the rectified image, if we consider the scan line is taken by a virtual 1D camera, then we can calibrate the 1D camera. Finally, the intrinsic parameters of the original camera can be determined from the intrinsic parameters of the virtual 1D camera. By evaluating on both simulated and real data we demonstrate that our method is efficient and robust.

This paper introduces a simultaneous estimation method of the extrinsic parameters of multiple fish-eye cameras using simple calibration markers. Precise extrinsic parameters of cameras mounted on a car are important, for example, to provide a seamless overhead view image to the driver. Calibration markers are set in the area that are observable from adjacent two cameras. Extrinsic parameters of each camera are estimated individually and then combined and refined using a geometric constraint. Cube markers are chosen as the calibration markers. The method is evaluated by simulation and experiments using a real car. It is shown that extrinsic parameters are obtained by the proposed method and suggested that the errors of intrinsic parameters affect the estimation of extrinsic parameters.

In this paper, a linear calibration method is proposed for a paracatadioptric camera using the images of two spheres. Two spheres are selected in space, and the two groups of their projection circles on the unit viewing sphere are made to intersect at four points. The quadrilateral consisting of four points is a rectangle, so a group of orthogonal directions can be determined in space to obtain a group of orthogonal vanishing points in the paracatadioptric image plane. Because of the relationship between orthogonal vanishing points and intrinsic camera parameters, the intrinsic parameters of a paracatadioptric camera can be linearly solved by at least five views satisfying the above conditions. First, one estimates the sphere images and their antipodal sphere images. Second, by solving the intersection of the images of two spheres and the intersection of the images of their antipodal spheres, a group of orthogonal vanishing points can be obtained in the image plane. Finally, by taking the relationship between the orthogonal vanishing points and the intrinsic camera parameters as constraint conditions, the intrinsic parameters of the paracatadioptric camera can be obtained. Simulation results and real image data demonstrate the effectiveness of our new algorithms.

This paper presents a case study towards intra-operative visualization of pre-operative medical images and surgery planning information. We describe a two stage procedure to provide a surgeon with valuable information during surgery, by augmenting live video with a 3D visualization of surgery planning. A first stage consists of automatic determination of the intrinsic parameters of the video camera by means of one image of a calibration object. It comprises an automatic ellipse extraction and a Solution to the 2D-3D correspondence problem. With the intrinsic camera parameters known, we perform a second stage to compute the extrinsic camera parameters with respect to the patient under surgery, allowing us to position and visualize the medical images in the right place. Techniques are investigated to visualize and manipulate the pre-operative medical images in order to cope with changes in the surgery scene.

<p>El cálculo de los parámetros intrínsecos y extrínsecos de cámaras digitales usadas en los cultivos de Tilapias, facilita el ajuste de dichas cámaras a las medidas biométricas reales que se esperan obtener del cultivo. Con estos cálculos se busca crear un modelo matemático que permita relacionar las medidas en pixeles del sensor óptico de las cámaras con las medidas biométricas de las tilapias. Para encontrar la talla de la tilapia roja por medio de reconocimiento de imágenes digitales se determinaron las características internas, geométricas y ópticas de la cámara (Parámetros intrínsecos) y la posición de referencia de la cámara, con respecto al acuario o estanque (parámetros extrínsecos). El propósito era hallar un modelo matemático que permitiera obtener la información biométrica de las tilapias a partir de las imágenes digitales obtenidas por la cámara. Para este caso, se aplicó el método de calibración de Zhang, utilizando imágenes de un plano con patrones de intensidad específicos, capturados por la cámara en diferentes posiciones y orientaciones. Con los parámetros encontrados se buscó el modelo matemático que relacionara las medidas reales biométricas de las tilapias con el análisis de las imágenes digitalizadas.</p>

In this paper, we present a self-calibration strategy to estimate camera intrinsic and extrinsic parameters using the scale-invariant feature transform (SIFT). The accuracy of the estimated parameters depends on how reliably a set of image correspondences is established. The SIFT employed in the self-calibration algorithms plays an important role in accurate estimation of camera parameters, because of its robustness to changes in viewing conditions. Under the assumption that the camera intrinsic parameters are constant, experimental results show that the SIFT-based approach using two images yields more competitive results than the existing Harris corner detector-based approach using two images.

Camera calibration using one-dimensional information and its applications in both controlled and uncontrolled environments

A new visual measurement method is proposed to estimate three-dimensional (3D) position of the object on the floor based on a single camera. The camera fixed on a robot is in an inclined position with respect to the floor. A measurement model with the camera's extrinsic parameters such as the height and pitch angle is described. Single image of a chessboard pattern placed on the floor is enough to calibrate the camera's extrinsic parameters after the camera's intrinsic parameters are calibrated. Then the position of object on the floor can be computed with the measurement model. Furthermore, the height of object can be calculated with the paired-points in the vertical line sharing the same position on the floor. Compared to the conventional method used to estimate the positions on the plane, this method can obtain the 3D positions. The indoor experiment testifies the accuracy and validity of the proposed method.

We present a camera calibration technique to determine the focal length and the extrinsic parameters of a camera merely by using one perspective view of two coplanar circles with arbitrary radii and topological configuration. We believe our effort is valuable, in that the position of a camera and its focal length are frequently adjusted in many vision applications. In contrast to the iterative optimization technique in previous work, whose convergence relies heavily on proper initialization, we propose a closed-form solution on the basis of simple matrix operations, which turns out to be computationally efficient. It can also be used to initialize existing algorithms for higher accuracy and speed. Our method is based on the projective equation of a circle, first presented here, which naturally encodes the intrinsic and extrinsic camera parameters and the degenerate conic envelope spanned by the image of circular points. Extensive experiments with both synthetic data and real images verify the efficiency and robustness of our technique.

Photogrammetry-based methods are traditionally used to estimate the geometrical parameters using optical images. These methods employ specific equipment, computationally sophisticated and expensive algorithms, and utilize projective geometry to reconstruct real-life scenes up to a scale. In this work, we used a computationally less-expensive method for sparse reconstruction to estimate the planar angles using two-view geometry and linear algorithms from non-orthogonal images acquired by a smartphone camera. First, intrinsic camera parameters were determined. Next, scale-invariant feature transform was used to identify the correspondence points from each pair of images. Epipolar constraint was applied on all these points to determine the essential matrix using the eight-point algorithm. Thereafter, extrinsic camera parameters were estimated from the essential matrix and combined with the intrinsic matrix to get the camera projection matrix. Finally, linear triangulation was used to get the sparse point cloud representing the scene. Planar angles were estimated by backprojecting the chosen image points and applying simple vector algebra on the obtained 3D points. The method was successful in estimating the planar angles in less than 10s on non-curved edges with an average error of 3% by using only ten images. Given the simplicity of methods used, this technique can be integrated into a smartphone for on-site measurements as well as large deformations.

Depth perception is crucial for many applications including robotics, UAV and autonomous driving. The visual sense, as well as cameras, map the 3D world on a 2D representation, losing the dimension representing depth. A way to recover 3D information from 2D images is to record and join data from multiple viewpoints. In case of a stereo setup, 4D data is gained. Existing methods to recover 3D information are computationally expensive. We propose a new, more intuitive method to recover 3D objects out of event-based stereo data, by using a Self-Organizing Map to solve the correspondence problem and establish a structure similar to a voxel grid. Our approach, as it is also computationally expensive, copes with performance issues by massive parallelization. Furthermore, the relatively small voxel grid makes this a memory friendly solution. This technique is very powerful as it does not need any prior knowledge of extrinsic and intrinsic camera parameters. Instead, those parameters and also the lens distortion are learned implicitly. Not only do we not require a parallel camera setup, as many existing methods, we do not even need any information about the alignment at all. We evaluated our method in a qualitative analysis and finding image correspondences.

An analytical method to calibrate a camera's extrinsic parameters and effective focal lengths is presented. We used a single image with four coplanar control lines. Unique solutions of the camera's extrinsic parameters and the two effective focal lengths are derived linearly and analytically. If the effective focal lengths are known, unique solutions of the extrinsic parameters can be derived more easily. Our method can easily be implemented and has better precision than the analogous method by use of control points. The results of simulated experiments are presented.

An improved outdoor camera calibration method based on Extended Kalman Filter (EKF) is proposed in this paper. GPS coordinate system is introduced to establish world coordinate system in the calibration procedure. According to the characteristics of GPS coordinate converted to plane coordinate and the camera calibration model, we establish the mapping relations of reference points between image plane coordinate and the world coordinate. Further, we consider the world coordinate error, set the world coordinates as the measure quantities, use the method of EKF to get the optimal estimation of intrinsic and extrinsic camera parameters, in EKF the camera's intrinsic and extrinsic parameters are set as state variables, the transformation equations of image coordinate to the world coordinate are set as the measurement equations, and we let the observed feature points' image coordinates and corresponding world coordinates as the input of the filter. Experimental results confirmed that the proposed method with high precision. Index Terms - camera calibration, GPS coordinate, EKF, world coordinate error