Journal ArticleDOI
The existence of geometrical density—image transformations corresponding to object motion
TLDR
Proofs are presented which show that under a few, easily realizable restrictions, there exists a geometrical image transformation that produces a change in a density image that is identical to the change that would be produced in the image by the motion of the objects being imaged.Abstract:
Proofs are presented which show that under a few, easily realizable restrictions, there exists a geometrical image transformation that produces a change in a density image that is identical to the change that would be produced in the image by the motion of the objects being imaged. Both two and three-dimensional images of three-dimensional scenes are considered subject to restrictions that are appropriate to conventional radiography, computerized axial tomography, gamma-ray scintigraphy, and magnetic resonance imaging. There is no restriction on the motion of the objects being imaged except that they behave as a conserved medium. There is no restriction on formation of the image except that it include convolution with a differentiable point-function. The convolution can be the result of blurring inherent in the acquisition of the image or of explicit image processing. The results have special significance with regard to the problem of motion artifacts in digital subtraction angiography. The case of incompressible flow is considered and comparisons are made with problems in optical flow.read more
Citations
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Journal ArticleDOI
Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint
TL;DR: The preliminary results suggest that incorporation of the incompressibility regularization term improves intensity-based free-form nonrigid registration of contrast-enhanced MR breast images by greatly reducing the problem of shrinkage of Contrast-enhancing structures while simultaneously allowing motion artifacts to be substantially reduced.
Journal ArticleDOI
Dense estimation of fluid flows
TL;DR: A dedicated minimization-based motion estimator based on an integrated version of the continuity equation of fluid mechanics, which is compatible with large displacements and associated with an original second-order div-curl regularization.
Reference BookDOI
Handbook of mathematical methods in imaging
TL;DR: In this article, the Mumford and Shah Model and its applications in total variation image restoration are discussed. But the authors focus on the reconstruction of 3D information, rather than the analysis of the image.
Book
Image Structure
TL;DR: This chapter considers the transformation (push forward) of a detector under an arbitrary spacetime automorphism, i.e. a "warping", or a smooth transformation of spacetime with smooth inverse, since by construction this may be all of Spacetime.
Journal Article
Fluid experimental flow estimation based on an optical-flow scheme. Experiments in fluids
TL;DR: In this paper, an image-based integrated version of the continuity equation is proposed to provide accurate dense motion fields, which preserve divergence and vorticity blobs of the motion field.
References
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Journal ArticleDOI
Determining optical flow
TL;DR: In this paper, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
Proceedings ArticleDOI
Determining Optical Flow
TL;DR: In this article, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
Book
Robot Vision
TL;DR: Robot Vision as discussed by the authors is a broad overview of the field of computer vision, using a consistent notation based on a detailed understanding of the image formation process, which can provide a useful and current reference for professionals working in the fields of machine vision, image processing, and pattern recognition.
Book
Introduction to the mechanics of a continuous medium
TL;DR: In this article, the authors propose a linearized theory of elasticity for tensors, which they call Linearized Theory of Elasticity (LTHE), which is based on tensors and elasticity.