We introduce a methodology for radiometric reconstruction (i.e. the simultaneous recovery of multiple illuminants and surface albedoes from multiple views), assuming that the geometry of the scene and of the cameras is known. We formulate a linear theory of multiple illuminants and show its similarity to the theory of geometric recovery of multiple views. Linear and nonlinear implementations are proposed, simulation results are discussed and, finally, results on real images are presented.

A method for reliably detecting change in the 3-D shape of objects that are well-modeled as single-value functions Z=f(x,y) is presented. It uses an estimate of the accuracy of the 3-D models derived from a set of images taken simultaneously. This accuracy estimate is used to distinguish between significant and insignificant changes in 3-D models derived from different image sets. The accuracy of the 3-D model is estimated using a general methodology, called self-consistency, for estimating the accuracy of computer vision algorithms, which does not require prior establishment of "ground truth". A novel image-matching measure based on Minimum Description Length (MDL) theory allows us to estimate the accuracy of individual elements of the 3-D model. Experiments to demonstrate the utility of the procedure are presented.

We propose a methodology to sketch the 3D geometry of an outdoor scene consisting of natural terrain. The method requires only a pair of uncalibrated images, but it produces a sketch where the order with respect to the dimensions of height above the ground plane and depth are correct. A dense representation is generated as a set of profile lines which overlays the original images.

We address in this paper the problem of self-calibration and metric reconstruction (up to a scale factor) from one unknown motion of an uncalibrated stereo rig. The epipolar constraint is first formulated for two uncalibrated images. The problem then becomes one of estimating unknowns such that the discrepancy from the epipolar constraint, in terms of sum of squared distances between points and their corresponding epipolar lines, is minimized. Although the full self-calibration is theoretically possible, we assume in this paper that the coordinates of the principal point of each camera are known. Then, the initialization of the unknowns can be done based on our previous work on self-calibration of a single moving camera, which requires to solve a set of so-called Kruppa equations. Redundancy of the information contained in a sequence of stereo images makes this method more robust than using a sequence of monocular images. Real data has been used to test the proposed method, and the results obtained are quite good. We also show experimentally that it is very difficult to estimate precisely the coordinates of the principal points of cameras. A variation of as high as several dozen pixels in the principal point coordinates does not affect significantly the 3-D reconstruction.

This paper describes two projects applying computer vision to Intelligent Vehicle Highway Systems. The first project has resulted in the development of a system for monitoring traffic scenes using video information. The objective is to estimate traffic parameters such as flow rates, speeds and link travel times, as well as to detect quickly disruptive incidents such as stalled vehicles and accidents. The second project is aimed at developing vision as a sensor technology for vehicle control. The novel feature of this project, compared to most previous approaches, is the extensive use of binocular stereopsis. First, it provides information for obstacle detection, grouping, and range estimation which is directly used for longitudinal control. Secondly, the obstacle-ground separation enables robust localization of partially occluded lane boundaries as v/ell as the dynamic update of camera rig parameters to deal with vibrations and vertical road curvature. 1

Proposes a new approach for vision-based longitudinal and lateral vehicle control. The novel feature of this approach is the use of binocular vision. We integrate two modules consisting of a new, domain-specific, efficient binocular stereo algorithm, and a lane marker detection algorithm, and show that the integration results in a improved performance for each of the modules. Longitudinal control is supported by detecting and measuring the distances to leading vehicles using binocular stereo. The knowledge of the camera geometry with respect to the locally planar road is used to map the images of the road plane in the two camera views into alignment. This allows us to separate image features into those lying in the road plane, e.g. lane markers, and those due to other objects which are dynamically integrated into an obstacle map. Therefore, in contrast with the previous work, we can cope with the difficulties arising from occlusion of lane markers by other vehicles. The detection and measurement of the lane markers provides us with the positional parameters and the road curvature which are needed for lateral vehicle control. Moreover, this information is also used to update the camera geometry with respect to the road, therefore allowing us to cope with the problem of vibrations and road inclination to obtain consistent results from binocular stereo.>

Studies experimentally the problem of computing the position of the epipoles in a pair of uncalibrated images. The approach, which is based on the definition of the epipolar transformation, exploits algebraic constraints obtained from point correspondences and provides a direct solution in which only the epipoles are involved. This is in opposition with the methods based on the computation of the fundamental matrix. In order to obtain a robust solution, three families of methods are successively considered: the first one uses statistics on closed-form solutions provided by the so-called Sturm method, the second one finds the intersection of plane cubics by deterministic procedures, and the third one is based on non-linear minimizations of a difference of cross-ratios. The authors discuss the shortcomings of each of these and show, using numerous experimental comparisons, that a drastic improvement can be obtained.

The problem of calibrating a camera is extremely important for practical applications. While classical work is based on the use of a calibration pattern whose 3D model is a priori known, self-calibration methods have also been investigated. These methods require only point matches obtained during unknown motions, without any a priori knowledge of the scenes. However, the method initially presented by Faugeras, Luong and Maybank (1992) was computationally expensive and sensitive to noise. In this paper, we propose an alternative method to compute at the same time camera calibration and motion, which is robust and efficient. This method allows also to take into account the important trinocular constraints. The practical applicability of our algorithm is illustrated with numerous real examples, which includes 3D reconstruction.

This paper proposes an algebraic method to generalize the usual equations of structure from motion, when calibration is not available. Contrary to previous approaches, the construction is made without any geometry and does not require a deep understanding of complex abstract objects. The construction being well formalized, an effective algorithm is easily derived, and experimental results are shown.

We address in this paper the problem of self-calibration and metric reconstruction (up to a scale) from one unknown motion of an uncalibrated stereo rig. The epipolar constraint is first formulated for two uncalibrated images. The problem then becomes one of estimating unknowns such that the discrepancy from the epipolar constraint, in terms of sum of squared distances between points and their corresponding epipolar lines, is minimized. Redundancy of the information contained in a sequence of stereo images makes this method more robust than using a sequence of monocular images. Real data have been used to test the proposed method, and the results obtained are quite good.

The fundamental matrix is a key concept when working with uncalibrated images and multiple viewpoints. It contains all the available geometric information and enables recovery of the epipolar geometry from uncalibrated perspective views. The problem of its determination from points which lie in several planes is discussed. In that case, there is an homography between coordinates of points in the two images. The use of different criteria to compute the homography is investigated. A very simple and important relation between the homography matrices obtained from the observation of planar surfaces and the fundamental matrix is then established. Using simulations and real images to validate this analysis, it is shown that as a first consequence of this relation, the general methods to compute the fundamental matrix are unstable when the points lie close to planes. New algorithms are proposed to exploit this situation through the use of the previous relation. Their performance is compared to the performance of the general algorithm using a large number of noisy synthetic data and real images.>

The problem of calibrating cameras is extremely important in computer vision. Existing work is based on the use of a calibration pattern whose 3D model is known a priori. The authors present a complete method for calibrating a camera, which requires only point matches from image sequences. The authors show, using experiments with noisy data, that it is possible to calibrate a camera just by pointing it at the environment, selecting points of interests, and tracking them in the image while moving the camera with an unknown motion. The camera calibration is computed in two steps. In the first step the epipolar transformation is found via the estimation of the fundamental matrix. The second step of the computation uses the so-called Kruppa equations, which link the epipolar transformation to the intrinsic parameters. These equations are integrated in an iterative filtering scheme.>