Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Kalman filter approach for accurate 3-D motion estimation from a sequence of stereo images
CVGIP: Image Understanding
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Recursive-batch estimation of motion and structure from monocular image sequences
CVGIP: Image Understanding
Model-based joint motion and structure estimation from stereo images
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense structure from a dense optical flow sequence
Computer Vision and Image Understanding
A Kalman Filter Approach to Direct Depth Estimation Incorporating Surface Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Digital Image Warping
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
3-D surface reconstruction from stereoscopic image sequences
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Direct Estimation of Structure and Motion from Multiple Frames
Direct Estimation of Structure and Motion from Multiple Frames
Direct methods for estimation of structure and motion from three views
Direct methods for estimation of structure and motion from three views
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We investigate the recovery of 3-D motion and structure from the stereo images of a stationary environment. A Kalman filter-based framework is proposed for the reconstruction of 3-D structure from multiple visual cues, through the integration of image motion and stereo disparity with the shading flow that is induced by the rotational motion of the source. A common scenario involves the coupled motion of artificial source(s) and stereo cameras that are installed on mobile submersible vehicles. Utilizing shading flow with the image motion leads to devising a more robust 3-D motion estimation algorithm, in addition to the important role in depth recovery/refinement by constraining the local surface gradients. Collectively, use of multiple cues enhances robustness with respect to perturbation in any of the cues. Results of experiments with synthetic and real imagery are presented to evaluate the performance of the proposed algorithm, including the construction of a composite 3-D depth map of an underwater scene from a sequence of stereo pairs.