Camera motion estimation through planar deformation determination

In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in the camera coordinate system. This condition is very restrictive but we show that provided translation and depth inverse variations are small enough, the error on optical flow involved by the approximation of depths by a constant is small. In this context, we propose a new model of camera motion, that allows to separate the image deformation in a similarity and a ``purely'' projective application, due to change of optical axis direction. This model leads to a quadratic approximation of image deformation that we estimate with an M-estimator; we can immediatly deduce camera motion parameters.Comment: 21 pages, version modifi\'ee accept\'e le 20 mars 200

3-D object modeling from 2-D occluding contour correspondences by opti-acoustic stereo imaging

•3-D object modeling from occluding contours from multi-modal optical/sonar stereo.•Imaging same contour by zero-baseline stereo, circumventing dense feature matching.•Improve reconstruction accuracy from 3-D contour positions and orientations.•Identifying degenerate configurations and simple misalignment rectification.•Flexibility to use navigation data or a few feature tracks for 3-D trajectory estimation. Utilizing in situ measurements to build 3-D volumetric object models under variety of turbidity conditions is highly desirable for marine sciences. To address the ineffectiveness of feature-based structure from motion and stereo methods under poor visibility, we explore a multi-modal stereo imaging technique that utilizes coincident optical and forward-scan sonar cameras, a so-called opti-acoustic stereo imaging system. The challenges of establishing dense feature correspondences in either opti-acoustic or low-contrast optical stereo images are avoided, by employing 2-D occluding contour correspondences, namely, the images of 3-D object occluding rims. Collecting opti-acoustic stereo pairs while circling an object, matching 2-D apparent contours in optical and sonar views to construct the 3-D occluding rim, and computing the stereo rig trajectory by opti-acoustic bundle adjustment, we generate registered samples of 3-D surface in a reference coordinate system. A surface interpolation gives the 3-D object model. In addition to the key advantage of utilizing range measurements from sonar, the proposed paradigm requires no assumption about local surface curvature as traditionally made in 3-D shape reconstruction from occluding contours. The reconstruction accuracy is improved by computing both the 3-D positions and local surface normals of sampled contours. We also present (1) a simple calibration method to estimate and correct for small discrepancy from the desired relative stereo pose; (2) an analytical analysis of the degenerate configuration that enables special treatment in mapping (tall) elongated objects with dominantly vertical edges. We demonstrate the performance of our method based on the 3-D surface rendering of certain objects, imaged by an underwater opti-acoustic stereo system

On feature extraction and region matching for forward scan sonar imaging

Automated processing of sonar video imagery enables valuable capabilities for a wide variety of underwater applications in turbid environments. Some key examples comprise the detection, localization and tracking of distinct scene targets, building feature maps, as well as improving positioning accuracy of unmanned submarines by means of image registration and 3D motion estimation to augment traditional positioning devices. This work offers a novel technique for the registration of 2D forward-look sonar images, by optimization over the sonar 3D motion parameters. It incorporates landmark detection through an adaptive clustering scheme with Gaussian map to represent key features at each frame. Improved performance is demonstrated in experiments with real data, both in terms of computation time and accuracy, relative to the state of the art, where the registration utilizes a simplified 2D image transformation model. Among many potentials, the method can improve precision in AUV navigation and environmental modeling

Dynamic scene analysis and mosaicing of benthic habitats by FS sonar imaging - Issues and complexities

Photo-mosaics generated automatically from as many as thousands of optical images have proved to be an effective technology to study the ecological patterns and dynamics of underwater ecosystems and benthic environments over spatial scales much larger than a single object or image. Unfortunately, optical systems, while useful in clear waters, are ineffective within environments with sources of turbidity and pollution, including lakes, marine sanctuaries, many ports and harbors. Two-dimensional high-resolution forward-scan imaging systems can serve as a suitable technology for constructing similar visual maps, provided that a range of complex imaging issues can be overcome. This paper investigates some of the complexities in analyzing dynamic events captured by a FS sonar imaging system when used in standard configuration to map the seafloor. Of special interest is the case of imaging targets at shorter ranges to maximize benthic object details. We give mathematical models that describe the dynamics associated with objects and shadows they cast on the seabed, and demonstrate some of these issues through examples from real data obtained in the lake on the University of Miami campus

Refining 3-D object models constructed from multiple FS sonar images by space carving

A sequential space carving method has been developed for the 3-D reconstruction of objects from multitude of forward-look sonar images captured at known sonar poses [1]. The 3-D space within common viewing volume of several camera poses is divided into small volumetric pixels (voxels). Projecting each onto various images, all voxels satisfying certain consistency measure are maintained. Conversely, voxels violating the consistency measure are carved out as inadmissible. The 3-D space comprising of all admissible voxels is taken as the potential object interior. Finally, the boundary of this volume defines the object surfaces. The method applying a binary admissibility criteria does not make use of image intensity values from any one of multitude of available sonar views. Here, the refinement of generated 3-D surface model is explored based on optimization formulations that minimize the discrepancy between the sonar intensity measurements and predicted values by the sonar image formation model. Comparison among various formulations leads to a selected one with desirable properties for improving the surface model