Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Tracking level sets by level sets: a method for solving the shape from shading problem
Computer Vision and Image Understanding
Optical-Flow Estimation while Preserving Its Discontinuities: A Variational Approach
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
Computer Tracking of Objects Moving in Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A variational approach to estimating 3D line orientation from motion is presented. A 2D motion constraint on 3D lines regularized by a quadratic term is used to set up an objective functional. From its associated Euler-Lagrange equations, we develop a vector-valued diffusion model, with a reaction term based on the 2D motion constraint. Three separate diffusion processes, corresponding to each component of the 3D line orientation, are coupled with each other through the reaction term and evolve simultaneously. Each 3D line orientation is estimated separately. The regularization parameter is estimated by an L-curve, which provides a better estimation. Experimental results from image sequences indicate stability and accuracy of the approach.