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.Comment: 9 pages, 5 figures Corrected errors in equation 1

Determining image distortion and PBS (point of best symmetry) in digital images using straight line matrices

[EN] It is impossible to take accurate measurements in photogrammetry without first removing the distortion in images. This paper presents a methodology for correcting radial and tangential distortion and for determining the PBS (Point of Best Symmetry) without knowledge of the interior orientation parameters (IOPs). An analytical plumb-line calibration method is used, measuring only the coordinates of points on straight lines, regardless of the position and direction of these lines within the image. Points belonging to multiple lines can also be used since the effects on their X and Y coordinates are calculated independently. The results obtained on an image of a common scene, taken with a handheld non-metric camera, show a high degree of accuracy even with a minimum number of observables. And its application on a calibrated grid for engineering purposes with a semi-metric camera, results optimal even using a single image. (C) 2016 Elsevier Ltd. All rights reserved.The authors wish to thank CITES Espana and Direccion General de Bienes Culturales y Ensenanzas Artisticas, de la Consejeria de Educacion, Cultura y Universidades de la Comunidad Autonoma de la Region de Murcia, Museo Nacional de Arqueologia Subacuatica. Financial support is gratefully acknowledged from Spanish "I + D + I MINECO" projects CTQ2011-28079-CO3-01 and 02 and CTQ2014-53736-C3-1-P supported by ERDEF funds. The authors also wish to thank Mr. Manuel Planes and Dr. Jose Luis Moya, technical supervisors of the Electron Microscopy Service of the Universitat Politecnica de Valencia.Herráez Boquera, J.; Denia Ríos, JL.; Navarro Esteve, PJ.; Rodríguez Pereña, J.; Martín Sánchez, MT. (2016). Determining image distortion and PBS (point of best symmetry) in digital images using straight line matrices. Measurement. 91:641-650. https://doi.org/10.1016/j.measurement.2016.05.051S6416509

Correcting distortion of image by image registration with the implicit function theorem

We propose a method for correcting image distortion due to camera lenses by calibrating intrinsic camera parameters. The proposed method is based on image registration and doesn't require point-to-point correspondence. Parameters of three successive transfor-mations –view change, radial distortion and illumination change– are estimated using the Gauss-Newton method. Estimating all 19 unknowns simultaneously, we introduce the implicit function theorem for calculating the Jacobian. To avoid local minima, we first estimate parameters for view change and employ coarse-to-fine minimization. Experimental results using real images demonstrate the robustness and the usefulness of the proposed method

MonoSLAM: Real-time single camera SLAM

Published versio

Universal Geometric Camera Calibration with Statistical Model Selection

We propose a new universal camera calibration approach that uses statistical information criteria for automatic camera model selection. It requires the camera to observe a planar pattern from different positions, and then closed-form estimates for the intrinsic and extrinsic parameters are computed followed by nonlinear optimization. In lieu of modeling radial distortion, the lens projection of the camera is modeled, and in addition we include decentering distortion. This approach is particularly advantageous for wide angle (fisheye) camera calibration because it often reduces the complexity of the model compared to modeling radial distortion. We then apply statistical information criteria to automatically select the complexity of the camera model for any lens type. The complete algorithm is evaluated on synthetic and real data for several different lens projections, and a comparison between existing methods which use radial distortion is done

Algorithms, Protocols & Systems for Remote Observation Using Networked Robotic Cameras

Emerging advances in robotic cameras, long-range wireless networking, and distributed sensors make feasible a new class of hybrid teleoperated/autonomous robotic remote "observatories" that can allow groups of peoples, via the Internet, to observe, record, and index detailed activity occurred in remote site. Equipped with robotic pan-tilt actuation mechanisms and a high-zoom lens, the camera can cover a large region with very high spatial resolution and allows for observation at a distance. High resolution motion panorama is the most nature data representation. We develop algorithms and protocols for high resolution motion panorama. We discover and prove the projection invariance and achieve real time image alignment. We propose a minimum variance based incremental frame alignment algorithm to minimize the accumulation of alignment error in incremental image alignment and ensure the quality of the panorama video over the long run. We propose a Frame Graph based panorama documentation algorithm to manage the large scale data involved in the online panorama video documentation. We propose a on-demand high resolution panorama video-streaming system that allows on-demand sharing of a high-resolution motion panorama and efficiently deals with multiple concurrent spatial-temporal user requests. In conclusion, our research work on high resolution motion panorama have significantly improve the efficiency and accuracy of image alignment, panorama video quality, data organization, and data storage and retrieving in remote observation using networked robotic cameras