Results 231 to 240 of about 55,702 (256)

Estimating optical flow by tracking contours

Pattern Recognition Letters, 1997
Abstract We present a novel method of velocity field estimation for the points on moving contours in a 2-D image sequence. The method determines the corresponding point in a next image frame by minimizing the curvature change of a given contour point. As a first step, snakes are used to locate smooth curves in 2-D imagery.
Park, JS, Han, JH
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A Robust Algorithm for Optical Flow Estimation

Computer Vision and Image Understanding, 1995
Abstract Most of the existing methods for optical flow estimation are based on a constraint equation which is defined for each image pixel. This class of algorithms is usually called gradient-based. Due to the structure of the constraint equation, the problem is ill-posed, thus some solutions based on regularization have been proposed in the past. On
NESI, PAOLO, DEL BIMBO, ALBERTO, D. Ben Tzvi   +2 more
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Optical Flow Estimation

2005
This chapter provides a tutorial introduction to gradient-based optical flow estimation. We discuss least-squares and robust estimators, iterative coarse-to-fine refinement, different forms of parametric motion models, different conservation assumptions, probabilistic formulations, and robust mixture models.
D. Fleet, Y. Weiss
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A MRF approach to optical flow estimation

Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003
A Markov random field (MRF) formulation for the problem of optical flow computation is studied. An adaptive window matching scheme is used to obtain a good measure of the correlation between the two images. A confidence measure for each match is also used. Thus, the input to the system is the adaptive correlation and the corresponding confidence.
John A. Vlontzos, Davi Geiger
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Optical Flow Estimation

2013
Optical flow is the velocity vector field of the projected environmental surfaces when a viewing system moves relative to the environment.
Amar Mitiche, J.K Aggarwal
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Optical Flow Estimation

2011
In this chapter we review the estimation of the two-dimensional apparent motion field of two consecutive images in an image sequence. This apparent motion field is referred to as optical flow field, a two-dimensional vector field on the image plane.
Andreas Wedel, Daniel Cremers
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A genetic algorithm for optical flow estimation

Image and Vision Computing, 2007
This paper illustrates a new optical flow estimation technique that builds upon a genetic algorithm (GA). First, the current frame is segmented into generic shape regions, using only luminance and color information. For each region, a two-parameter motion model is estimated using a GA. The fittest individuals identified at the end of this step are used
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Method for improving optical flow estimation

Journal of Electronic Imaging, 2018
A method is proposed that improves the robustness and accuracy of optical flow estimation in real complex scenes. The method overcomes the limitations incurred by illumination variations using a combination of the brightness constancy and gradient constancy.
Naigong Yu, Yue Chen, Yuling Zheng
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Consistent segmentation for optical flow estimation

Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005
In this paper, we propose a method for jointly computing optical flow and segmenting video while accounting for mixed pixels (matting). Our method is based on statistical modeling of an image pair using constraints on appearance and motion. Segments are viewed as overlapping regions with fractional (alpha) contributions.
C. Lawrence Zitnick, Nebojsa Jojic, Sing Bing Kang   +2 more
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A framework for the robust estimation of optical flow

1993 (4th) International Conference on Computer Vision, 2002
The authors consider the problem of robustly estimating optical flow from a pair of images using a new framework based on robust estimation which addresses violations of the brightness constancy and spatial smoothness assumptions. They also show the relationship between the robust estimation framework and line-process approaches for coping with spatial
Michael J. Black, P. Anandan 0001
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