Comutations underlying the measuremnt of visual motion.
Artificial Intelligence
The image flow constraint equation
Computer Vision, Graphics, and Image Processing
Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Flow Segmentation and Estimation by Constraint Line Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
The analytic structure of image flows: deformation and segmentation
Computer Vision, Graphics, and Image Processing
Adaptive estimation of optical flow from general object motion
SAC '92 Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing: technological challenges of the 1990's
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The authors present an iterative algorithm for the recovery of 2-D motion, i.e., for the determination of a transformation that maps one image onto another. The local ambiguity in measuring the motion of contour segments (the aperture problem) implies a reliance on measurements along the normal direction. Since the measured normal flow does not agree with the actual normal flow, the full flow recovered from this erroneous flow also possesses substantial error, and any attempt to recover the 3-D motion from such full flow fails. The proposed method is based on the observation that a polynomial approximation of the image flow provides sufficient information for 3-D motion computation. The use of an explicit flow model results in improved normal flow estimates through an iterative process. The authors discuss the adequacy and the convergence of the algorithm. The algorithm was tested on some synthetic and some simple natural time-varying images. The image flow recovered from this scheme is sufficiently accurate to be useful in 3-D structure and motion computation.