3-D motion estimation, understanding, and prediction from nosiy image sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
A General Motion Model and Spatio-Temporal Filters forComputing Optical Flow
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multi-Frame Structure-from-Motion Algorithm under Perspective Projection
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Robustly estimating changes in image appearance
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Model-Based Brightness Constraints: On Direct Estimation of Structure and Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing Optical Flow with Physical Models of Brightness Variation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Motion and Structure Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Recovery of Planar-Parallax from Multiple Frames
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Parallax Geometry of Pairs of Points for 3D Scene Analysis
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
A Hierarchical Approach for Obtaining Structure from Two-Frame Optical Flow
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Direct recovery of motion and range from images of scenes with time-varying illumination
ISCV '95 Proceedings of the International Symposium on Computer Vision
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Accurate dense optical flow estimation using adaptive structure tensors and a parametric model
IEEE Transactions on Image Processing
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We present an iterative algorithm for robustly estimating the ego-motion and refining and updating a coarse depth map using surface parallax and a generalized dynamic image (GDI) model. Given a coarse depth map acquired by a range-finder or extracted from a Digital Elevation Map (DEM), we first estimate the ego-motion by combining a global ego-motion constraint and a local GDI model. Using the estimated camera motion and the available depth estimate, motion of the 3D points is compensated. We utilize the fact that the resulting surface parallax field is an epipolar field and constrain its direction using the previous motion estimates. We then estimate the magnitude of the parallax field and the GDI model parameters locally and use them to refine the depth map estimates. We use a tensor based approach to formulate the depth refinement procedure as an eigen-value problem and obtain confidence measures for determining the accuracy of the estimated depth values. These confidence measures are used to remove regions with potentially incorrect depth estimates for robustly estimating ego-motion in the next iteration. Experimental results using both synthetic and real data are presented. Comparisons with results obtained using a brightness constancy (BC) model show that the proposed algorithm works significantly better when time-varying illumination changes are present in the scene.