Hicham Sekkati

Opti-Acoustic Stereo Imaging: On System Calibration and 3-D Target Reconstruction

Utilization of an acoustic camera for range measurements is a key advantage for 3-D shape recovery of underwater targets by opti-acoustic stereo imaging, where the associated epipolar geometry of optical and acoustic image correspondences can be described in terms of conic sections. In this paper, we propose methods for system calibration and 3-D scene reconstruction by maximum likelihood estimation from noisy image measurements. The recursive 3-D reconstruction method utilized as initial condition a closed-form solution that integrates the advantages of two other closed-form solutions, referred to as the range and azimuth solutions. Synthetic data tests are given to provide insight into the merits of the new target imaging and 3-D reconstruction paradigm, while experiments with real data confirm the findings based on computer simulations, and demonstrate the merits of this novel 3-D reconstruction paradigm.

Optical Flow 3D Segmentation and Interpretation: A Variational Method with Active Curve Evolution and Level Sets

This study investigates a variational, active curve evolution method for dense three-dimensional (3D) segmentation and interpretation of optical flow in an image sequence of a scene containing moving rigid objects viewed by a possibly moving camera. This method jointly performs 3D motion segmentation, 3D interpretation (recovery of 3D structure and motion), and optical flow estimation. The objective functional contains two data terms for each segmentation region, one based on the motion-only equation which relates the essential parameters of 3D rigid body motion to optical flow, and the other on the Horn and Schunck optical flow constraint. It also contains two regularization terms for each region, one for optical flow, the other for the region boundary. The necessary conditions for a minimum of the functional result in concurrent 3D-motion segmentation, by active curve evolution via level sets, and linear estimation of each region essential parameters and optical flow. Subsequently, the screw of 3D motion and regularized relative depth are recovered analytically for each region from the estimated essential parameters and optical flow. Examples are provided which verify the method and its implementation

Joint optical flow estimation, segmentation, and 3D interpretation with level sets

This paper describes a variational method with active curve evolution and level sets for the estimation, segmentation, and 3D interpretation of optical flow generated by independently moving rigid objects in space. Estimation, segmentation, and 3D interpretation are performed jointly. Segmentation is based on an estimate of optical flow consistent with a single rigid motion in each segmentation region. The method, which allows both viewing system and viewed objects to move, results in three steps iterated until convergence: (a) evolution of closed curves via level sets and, in each region of the segmentation, (b) linear least squares computation of the essential parameters of rigid motion, (c) estimation of optical flow consistent with a single rigid motion. The translational and rotational components of rigid motion and regularized relative depth are recovered analytically for each region of the segmentation from the estimated essential parameters and optical flow. Several examples with real image sequences are provided which verify the validity of the method.

3-D Motion Estimation for Positioning from 2-D Acoustic Video Imagery

We address the problem of estimating 3-D motion from acoustic images acquired by high-frequency 2-D imaging sonars deployed in underwater. Utilizing a planar approximation to scene surfaces, two-view homography is the basis of a nonlinear optimization method for estimating the motion parameters. There is no scale factor ambiguity, unlike the case of monocular motion vision for optical images. Experiments with real images demonstrate the potential in a range of applications, including target-based positioning in search and inspection operations.

Concurrent 3-D motion segmentation and 3-D interpretation of temporal sequences of monocular images

The purpose of this study is to investigate a variational method for joint multiregion three-dimensional (3-D) motion segmentation and 3-D interpretation of temporal sequences of monocular images. Interpretation consists of dense recovery of 3-D structure and motion from the image sequence spatiotemporal variations due to short-range image motion. The method is direct insomuch as it does not require prior computation of image motion. It allows movement of both viewing system and multiple independently moving objects. The problem is formulated following a variational statement with a functional containing three terms. One term measures the conformity of the interpretation within each region of 3-D motion segmentation to the image sequence spatiotemporal variations. The second term is of regularization of depth. The assumption that environmental objects are rigid accounts automatically for the regularity of 3-D motion within each region of segmentation. The third and last term is for the regularity of segmentation boundaries. Minimization of the functional follows the corresponding Euler-Lagrange equations. This results in iterated concurrent computation of 3-D motion segmentation by curve evolution, depth by gradient descent, and 3-D motion by least squares within each region of segmentation. Curve evolution is implemented via level sets for topology independence and numerical stability. This algorithm and its implementation are verified on synthetic and real image sequences. Viewers presented with anaglyphs of stereoscopic images constructed from the algorithms output reported a strong perception of depth.

Opti-Acoustic Stereo Imaging, System Calibration and 3-D Reconstruction

Utilization of an acoustic camera for range measurements is a key advantage for 3-D shape recovery of underwater targets by opti-acoustic stereo imaging, where the associated epipolar geometry of optical and acoustic image correspondences can be described in terms of conic sections. In this paper, we propose methods for system calibration and 3-D scene reconstruction by maximum likelihood estimation from noisy image measurements. The recursive 3-D reconstruction method utilized as initial condition a closed-form solution that integrates the advantages of so-called range and azimuth solutions. Synthetic data tests are given to provide insight into the merits of the new target imaging and 3-D reconstruction paradigm, while experiments with real data confirm the findings based on computer simulations, and demonstrate the merits of this novel 3-D reconstruction paradigm.

Dense 3D interpretation of image sequences: a variational approach using anisotropic diffusion

The purpose of this study is to investigate a new method for recovering relative depth and 3D motion from a temporal sequence of monocular images. The method is direct insomuch as it does not require computation of image motion prior to 3D interpretation. This interpretation is obtained by minimizing a functional with two characteristic terms, one of conformity to the spatiotemporal changes in the image sequence, the other of regularization based on anisotropic diffusion. The Euler-Lagrange equations corresponding to the functional minimization are solved iteratively via the half-quadratic algorithm.

A variational method for the recovery of dense 3D structure from motion

The purpose of this study is to investigate a variational formulation of the problem of three-dimensional (3D) interpretation of temporal image sequences based on the 3D brightness constraint and anisotropic regularization. The method allows movement of both the viewing system and objects and does not require the computation of image motion prior to 3D interpretation. Interpretation follows the minimization of a functional with two terms: a term of conformity of the 3D interpretation to the image sequence first-order spatio-temporal variations, and a term of regularization based on anisotropic diffusion to preserve the boundaries of interpretation. The Euler-Lagrange partial differential equations corresponding to the functional are solved efficiently via the half-quadratic algorithm. Results of several experiments on synthetic and real image sequences are given to demonstrate the validity of the method and its implementation.

Framework for Natural Landmark-based Robot Localization

In this paper we present a framework for vision-based robot localization using natural planar landmarks. Specifically, we demonstrate our framework with planar targets using Fern classifiers that have been shown to be robust against illumination changes, perspective distortion, motion blur, and occlusions. We add stratified sampling in the image plane to increase robustness of the localization scheme in cluttered environments and on-line checking for false detection of targets to decrease false positives. We use all matching points to improve pose estimation and an off-line target evaluation strategy to improve a priori map building. We report experiments demonstrating the accuracy and speed of localization. Our experiments entail synthetic and real data. Our framework and our improvements are however more general and the Fern classifier could be replaced by other techniques.

Direct and Indirect 3-D Reconstruction from Opti-Acoustic Stereo Imaging

Utilization of an acoustic camera for range measurements is a significant advantage for 3D shape recovery of underwater targets by opti-acoustic stereo imaging, where the associated epipolar geometry of visual and acoustic image correspondences is described in terms of conic sections and trigonometric functions. In this paper, we propose and analyze a number of methods based on direct and indirect approaches that provide insight on the merits of the new imaging and 3D object reconstruction paradigm. We have devised certain indirect methods, built on a regularization formulation, to first compute from noisy correspondences maximum likelihood estimates that satisfy the epipolar geometry. The 3D target points can then be determined from a number of closed-form solutions applied to these ML estimates. An alternative direct approach is also presented for 3D reconstruction directly from noisy correspondences. Computer simulations verify consistency between the analytical and experimental reconstruction SNRs - the criterion applied in performance assessment of these various solutions.