Estimating Geo-temporal Location of Stationary Cameras Using Shadow Trajectories

Abstract. Using only shadow trajectories of stationary objects in a scene, we demonstrate that using a set of six or more photographs are sufficient to ac-curately calibrate the camera. Moreover, we present a novel application where, using only three points from the shadow trajectory of the objects, one can ac-curately determine the geo-location of the camera, up to a longitude ambiguity, and also the date of image acquisition without using any GPS or other special instruments. We refer to this as “geo-temporal localization”. We consider possi-ble cases where ambiguities can be removed if additional information is avail-able. Our method does not require any knowledge of the date or the time when the pictures are taken, and geo-temporal information is recovered directly from the images. We demonstrate the accuracy of our technique for both steps of cali-bration and geo-temporal localization using synthetic and real data.

Fast Outlier Rejection by Using Parallax-Based Rigidity Constraint for Epipolar Geometry Estimation

A novel approach is presented in order to reject correspondence outliers between frames using the parallax-based rigidity constraint for epipolar geometry estimation. In this approach, the invariance of 3-D relative projective structure of a stationary scene over different views is exploited to eliminate outliers, mostly due to independently moving objects of a typical scene. The proposed approach is compared against a well-known RANSAC-based algorithm by the help of a test-bed. The results showed that the speed-up, gained by utilization of the proposed technique as a preprocessing step before RANSAC-based approach, decreases the execution time of the overall outlier rejection, significantly

Autocalibration with the Minimum Number of Cameras with Known Pixel Shape

In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.Comment: 19 pages, 14 figures, 7 tables, J. Math. Imaging Vi

Monocular Vision based Navigation in GPS-Denied Riverine Environments

This paper presents a new method to estimate the range and bearing of landmarks and solve the simultaneous localization and mapping (SLAM) problem. The proposed ranging and SLAM algorithms have application to a micro aerial vehicle (MAV) flying through riverine environments which occasionally involve heavy foliage and forest canopy. Monocular vision navigation has merits in MAV applications since it is lightweight and provides abundant visual cues of the environment in comparison to other ranging methods. In this paper, we suggest a monocular vision strategy incorporating image segmentation and epipolar geometry to extend the capability of the ranging method to unknown outdoor environments. The validity of our proposed method is verified through experiments in a river-like environment

Optimal Estimation of Matching Constraints

International audienceWe describe work in progress on a numerical library for estimating multi-image matching constraints, or more precisely the multi-camera geometry underlying them. The library will cover several variants of homographic, epipolar, and trifocal constraints, using various different feature types. It is designed to be modular and open-ended, so that (i) new feature types or error models, (ii) new constraint types or parametrizations, and (iii) new numerical resolution methods, are relatively easy to add. The ultimate goal is to provide practical code for stable, reliable, statistically optimal estimation of matching geometry under a choice of robust error models, taking full account of any nonlinear constraints involved. More immediately, the library will be used to study the relative performance of the various competing problem parametrizations, error models and numerical methods. The paper focuses on the overall design, parametrization and numerical optimization issues. The methods described extend to many other geometric estimation problems in vision, e.g. curve and surface fitting

Self-calibration and motion recovery from silhouettes with two mirrors

LNCS v. 7724-7727 (pts. 1-4) entitled: Computer vision - ACCV 2012: 11th Asian Conference on Computer Vision ... 2012: revised selected papersThis paper addresses the problem of self-calibration and motion recovery from a single snapshot obtained under a setting of two mirrors. The mirrors are able to show five views of an object in one image. In this paper, the epipoles of the real and virtual cameras are firstly estimated from the intersection of the bitangent lines between corresponding images, from which we can easily derive the horizon of the camera plane. The imaged circular points and the angle between the mirrors can then be obtained from equal angles between the bitangent lines, by planar rectification. The silhouettes produced by reflections can be treated as a special circular motion sequence. With this observation, technique developed for calibrating a circular motion sequence can be exploited to simplify the calibration of a single-view two-mirror system. Different from the state-of-the-art approaches, only one snapshot is required in this work for self-calibrating a natural camera and recovering the poses of the two mirrors. This is more flexible than previous approaches which require at least two images. When more than a single image is available, each image can be calibrated independently and the problem of varying focal length does not complicate the calibration problem. After the calibration, the visual hull of the objects can be obtained from the silhouettes. Experimental results show the feasibility and the preciseness of the proposed approach. © 2013 Springer-Verlag.postprin