IEEE Transactions on Pattern Analysis and Machine Intelligence
A simplified linear optic-flow motion algorithm
Computer Vision, Graphics, and Image Processing
Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Analysis of Inherent Ambiguities in Recovering 3-D Motion from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Distributed representation and analysis of visual motion
Distributed representation and analysis of visual motion
Performance of optical flow techniques
International Journal of Computer Vision
Computational analysis of visual motion
Computational analysis of visual motion
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
3-D motion estimation from motion field
Artificial Intelligence - Special volume on computer vision
On the Geometry of Visual Correspondence
International Journal of Computer Vision
Motion and Structure from Image Sequences
Motion and Structure from Image Sequences
What Can Projections of Flow Fields Tell Us About the Visual Motion
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Fast and Accurate Algorithms for Projective Multi-Image Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Structure-from-Motion Ambiguity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact Two-Image Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Approximation and Rate-Distortion Analysis for Robust Structure and Motion Estimation
International Journal of Computer Vision
Face reconstruction from monocular video using uncertainty analysis and a generic model
Computer Vision and Image Understanding - Special issue on Face recognition
Pose and Motion Recovery from Feature Correspondences and a Digital Terrain Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Joint optical flow estimation, segmentation, and 3D interpretation with level sets
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A variational method for the recovery of dense 3D structure from motion
Robotics and Autonomous Systems
Adjusting route panoramas with condensed image slices
Proceedings of the 15th international conference on Multimedia
Scanning Depth of Route Panorama Based on Stationary Blur
International Journal of Computer Vision
Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition
Foundations and Trends in Signal Processing
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In this paper, we present a globally optimal and computationally efficient technique for estimating the focus of expansion (FOE) of an optical flow field, using fast partial search. For each candidate location on a discrete sampling of the image area, we generate a linear system of equations for determining the remaining unknowns, viz. rotation and inverse depth. We compute the least squares error of the system without actually solving the equations, to generate an error surface that describes the goodness of fit across the hypotheses. Using Fourier techniques, we prove that given an N × N flow field, the FOE, and subsequently rotation and structure, can be estimated in {\cal O}(N^2 log N) operations. Since the resulting system is linear, bounded perturbations in the data lead to bounded errors.We support the theoretical development and proof of our technique with experiments on synthetic and real data. Through a series of experiments on synthetic data, we prove the correctness, robustness and operating envelope of our algorithm. We demonstrate the utility of our technique by applying it for detecting obstacles from a monocular sequence of images.