Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Flow with an Intensity-Weighted Smoothing
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Kinematics and Structure of a Rigid Object from a Sequence of Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Contour-Based Recovery of Image Flow: Iterative Transformation Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Two Perspective Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive Filters for Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
The computation of optical flow
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A General Motion Model and Spatio-Temporal Filters forComputing Optical Flow
International Journal of Computer Vision
Improved Accuracy in Gradient-Based Optical Flow Estimation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting Discontinuities in Optical Flow
International Journal of Computer Vision
Temporal Multi-Scale Models for Flow and Acceleration
International Journal of Computer Vision
Design and Use of Linear Models for Image Motion Analysis
International Journal of Computer Vision
Optical Flow in Log-Mapped Image Plane-A New Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Accuracy of the Computation of Optical Flow and of the Recovery of Motion Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Motion and Structure Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Flow in Log-mapped Image Plane (A New Approach)
RobVis '01 Proceedings of the International Workshop on Robot Vision
Combining the Advantages of Local and Global Optic Flow Methods
Proceedings of the 24th DAGM Symposium on Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
On robustness and localization accuracy of optical flow computation for underwater color images
Computer Vision and Image Understanding
Using quasi-continuous histograms for fuzzy main motion estimation in video sequence
Fuzzy Sets and Systems
The Asymmetry of Image Registration and Its Application to Face Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Methods and Programs in Biomedicine
Building Blocks for Computer Vision with Stochastic Partial Differential Equations
International Journal of Computer Vision
Motion-Compensated Frame Interpolation for Intra-Mode Blocks
IEICE - Transactions on Information and Systems
TGSF/TLoG filter with optical flow technique for large motion detection
Machine Graphics & Vision International Journal
Robust fault matched optical flow detection using 2d histogram
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
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Multiple views of a scene can provide important information about the structure and dynamic behavior of three-dimensional objects. Many of the methods that recover this information require the determination of optical flow-the velocity, on the image, of visible points on object surfaces. An important class of techniques for estimating optical flow depend on the relationship between the gradients of image brightness. While gradient-based methods have been widely studied, little attention has been paid to accuracy and reliability of the approach. Gradient-based methods are sensitive to conditions commonly encountered in real imagery. Highly textured surfaces, large areas of constant brightness, motion boundaries, and depth discontinuities can all be troublesome for gradient-based methods. Fortunately, these problematic areas are usually localized can be identified in the image. In this paper we examine the sources of errors for gradient-based techniques that locally solve for optical flow. These methods assume that optical flow is constant in a small neighborhood. The consequence of violating in this assumption is examined. The causes of measurement errors and the determinants of the conditioning of the solution system are also considered. By understanding how errors arise, we are able to define the inherent limitations of the technique, obtain estimates of the accuracy of computed values, enhance the performance of the technique, and demonstrate the informative value of some types of error.