A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A linear algorithm for motion estimation using straight line correspondences
Computer Vision, Graphics, and Image Processing
Stable adaptive systems
A simplification to linear two-view motion algorithms
Computer Vision, Graphics, and Image Processing
Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure from motion using line correspondences
International Journal of Computer Vision
Motion from point matches: multiple of solutions
International Journal of Computer Vision
On the motion of 3-D curves and its relationship to optical flow
ECCV 90 Proceedings of the first european conference on Computer vision
Shape from shading
Polynomial Methods for Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in the machine interpretation of visual motion
Experiments in the machine interpretation of visual motion
A multi-frame approach to visual motion perception
International Journal of Computer Vision
A Perspective Theory for Motion and Shape Estimation in Machine Vision
SIAM Journal on Control and Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Vision
Theory of Reconstruction from Image Motion
Theory of Reconstruction from Image Motion
Group Theoretical Methods in Image Understanding
Group Theoretical Methods in Image Understanding
Recursive Estimation of 3D Motion and Surface Structure from Local Affine Flow Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge and Curve Detection for Visual Scene Analysis
IEEE Transactions on Computers
Modeling and Estimation of the Dynamics of Planar Algebraic Curves via Riccati Equations
Journal of Mathematical Imaging and Vision
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The problem of estimating motion and structure from a sequence of images has been a major research theme in machine vision for many years and remains one of the most challenging ones. In this work, we use sliding mode observers to estimate the motion and the structure of a moving body with the aid of a change-coupled device (CCD) camera. We consider a variety of dynamical systems which arise in machine vision applications and develop a novel identification procedure for the estimation of both constant and time-varying parameters. The basic procedure introduced for parameter estimation is to recast image feature dynamics linearly in terms of unknown parameters and construct a sliding mode observer to produce asymptotically correct estimates of the observed image features, and then use the observer input to compute parameters. Much of our analysis has been substantiated by computer simulations and real experiments.