Object triangulation, 3-D object tracking, feature correspondence, and camera calibration are key problems for estimation from camera networks. This paper addresses these problems within a unified Bayesian framework for joint multi-object tracking and sensor registration. Given that using standard filtering approaches for state estimation from cameras is problematic, an alternative parametrisation is exploited, called disparity space. The disparity space-based approach for triangulation and object tracking is shown to be more effective than non-linear versions of the Kalman filter and particle filtering for non-rectified cameras. The approach for feature correspondence is based on the Probability Hypothesis Density (PHD) filter, and hence inherits the ability to update without explicit measurement association, to initiate new targets, and to discriminate between target and clutter. The PHD filtering approach then forms the basis of a camera calibration method from static or moving objects. Results are shown on simulated data.
@cite_30: In this paper we explore a multiple hypothesis approach to estimating rigid motion from a moving stereo rig. More precisely, we introduce the use of Gaussian mixtures to model correspondence uncertainties for disparity and velocity (optical flow) estimation. We show some properties of the disparity space and show how rigid transformations can be represented. An algorithm derived from standard random sampling-based robust estimators, that efficiently estimates rigid transformations from multi-hypothesis disparity maps and velocity fields is given.
@cite_35: Real-time stereovision systems play an important role in automotive related applications. This paper concerns the problem of rigid motion estimation with a stereovision sensor. Given a set of corresponding 3D points in Euclidean space reconstructed from stereovision, efficient linear algorithms exist to solve for the rigid motion. However it has been well known that the noise in the Euclidean reconstruction from stereovision is heteroscedastic and anisotropic, therefore the linear algorithms are only sub-optimal. Recently a d-motion based algorithm has been developed to solve for the rigid motion directly in the disparity space, in which the noise can be approximated to be homogenous and isotropic. However this algorithm is nonlinear and requires iterative least-squares solution. By reformulating the problem, a closed-form linear solution is presented in this paper to solve for the rigid motion in disparity space. Synthetic experimental results show that this new algorithm outperforms the d-motion based algorithm in terms of both accuracy and computational cost. We believe that the closed-form linear solution is potentially very useful for applications making use of stereovision to estimate rigid motion.
@cite_29: This classic work on cyclopean perception has influenced a generation of vision researchers, cognitive scientists, and neuroscientists and has inspired artists, designers, and computer graphics pioneers. In Foundations of Cyclopean Perception (first published in 1971 and unavailable for years), Bela Julesz traced the visual information flow in the brain, analyzing how the brain combines separate images received from the two eyes to produce depth perception. Julesz developed novel tools to do this: random-dot stereograms and cinematograms, generated by early digital computers at Bell Labs. These images, when viewed with the special glasses that came with the book, revealed complex, three-dimensional surfaces; this mode of visual stimulus became a paradigm for research in vision and perception. This reprint edition includes all 48 color random-dot designs from the original, as well as the special 3-D glasses required to view them.Foundations of Cyclopean Perception has had a profound impact on the vision studies community. It was chosen as one of the one hundred most influential works in cognitive science in a poll conducted by the University of Minnesota's Center for Cognitive Sciences. Many copies are "permanently borrowed" from college libraries; used copies are sought after online. Now, with this facsimile of the 1971 edition, the book is available again to cognitive scientists, neuroscientists, vision researchers, artists, and designers.
@cite_17: Image-based novel-view synthesis requires dense correspondences between the original views to produce a high quality synthetic view. In a wide-baseline stereo setup, dense correspondences are difficult to achieve due to the significant change in viewpoint giving rise to a number of problems. To improve their quality, the original, incomplete disparity maps are usually interpolated to fill in the missing regions. When the data is very sparse, such as in the case of the wide-baseline stereo, interpolation alone is not enough. Instead, a 3-D model of the scene can be used to fill in the missing regions more reliably, using a-priori knowledge. However, the 3-D model can be used more efficiently and accurately in disparity space, where the disparity data originates from. In this paper we present and compare the two techniques. We show that, in comparison with the 3-D approach, the disparity space approach offers a computationally less expensive and potentially more accurate solution. (10 pages)
@cite_25: 
@cite_11: We describe a real-time, low-cost system to localize a mobile robot in outdoor environments. Our system relies on stereo vision to robustly estimate frame-to-frame motion in real time (also known as visual odometry). The motion estimation problem is formulated efficiently in the disparity space and results in accurate and robust estimates of the motion even for a small-baseline configuration. Our system uses inertial measurements to fill in motion estimates when visual odometry fails. This incremental motion is then fused with a low-cost GPS sensor using a Kalman Filter to prevent long-term drifts. Experimental results are presented for outdoor localization in moderately sized environments ( 100 meters)
The concept of binocular disparity, defined by the difference in the location of an object in two images, arose from research into mammalian visual systems to reflect the horizontal separation of the left and right eyes @cite_29 . Perception of depth is obtained in stereopsis as a consequence of this binocular disparity. The same concept is applied to problems in computer vision for extracting depth information from stereo cameras and researchers have designed algorithms for 3-D estimation from cameras by considering the disparity space as a state space @cite_11 @cite_30 @cite_35 @cite_25 @cite_17 .