IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
`Four-line' method of locally estimating optic flow
Image and Vision Computing - Special issue: papers from the second Alvey Vision Conference
Investigations of multigrid algorithms for the estimation of optical flow fieldsin image sequences
Computer Vision, Graphics, and Image Processing
Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection
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
Optical Flow with an Intensity-Weighted Smoothing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Two Perspective Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measurement of Visual Motion
Digital Picture Processing
Measuring visual motion from image sequences
Measuring visual motion from image sequences
Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal-flow minimum-cost correspondence assignment in particle flow tracking
Computer Vision and Image Understanding
Tracking of sea and air flows from sequential satellite images by the relaxation-contour method
Pattern Recognition and Image Analysis
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Optical flow estimation is discussed based on a model for time-varying images more general than that implied in the work of Horn and Schunk [21]. The emphasis is on applications where low contrast imagery, non-rigid or evolving object patterns movement, as well as large interframe displacements are encountered. Template matching is identified as having advantages over point correspondence and the gradient-based approach in dealing with such applications. The two fundamental uncertainties in feature matching procedures, whether it is template matching or feature point correspondences, are discussed. Correlation template matching procedures are established based on likelihood measurement. A new method for determining optical flow is developed by combining template matching and relaxation labeling. In this method, a number of candidate displacements for each template and their respective likelihood measures are first determined. Then, relaxation labeling is employed to iteratively update each candidate驴s likelihood by requiring smoothness within a motion field. Real cloud images taken from meteorological satellites are used to test the usefulness of this method. It is shown in this application that the new method can deal effectively with the uncertainty of multiple peak (multi-modal) correlation surfaces encountered in template matching. The results show significant improvement when compared to that of the maximum cross correlation (MCC), which has been operationally used for cloud tracking, and to that of the method of Barnard and Thompson, which estimates displacements based on combining point correspondences with relaxation labeling.