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Highly accurate optic flow computation with theoretically justified warping

TLDR
In this article, a variational model for optic flow computation based on non-linearised and higher order constancy assumptions is proposed, which is also capable of dealing with large displacements.
Abstract
In this paper, we suggest a variational model for optic flow computation based on non-linearised and higher order constancy assumptions. Besides the common grey value constancy assumption, also gradient constancy, as well as the constancy of the Hessian and the Laplacian are proposed. Since the model strictly refrains from a linearisation of these assumptions, it is also capable to deal with large displacements. For the minimisation of the rather complex energy functional, we present an efficient numerical scheme employing two nested fixed point iterations. Following a coarse-to-fine strategy it turns out that there is a theoretical foundation of so-called warping techniques hitherto justified only on an experimental basis. Since our algorithm consists of the integration of various concepts, ranging from different constancy assumptions to numerical implementation issues, a detailed account of the effect of each of these concepts is included in the experimental section. The superior performance of the proposed method shows up by significantly smaller estimation errors when compared to previous techniques. Further experiments also confirm excellent robustness under noise and insensitivity to parameter variations.

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Citations
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Book

Computer Vision: Algorithms and Applications

TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
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A Database and Evaluation Methodology for Optical Flow

TL;DR: This paper proposes a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms and analyzes the results obtained to date to draw a large number of conclusions.
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A duality based approach for realtime TV-L 1 optical flow

TL;DR: This work presents a novel approach to solve the TV-L1 formulation, which is based on a dual formulation of the TV energy and employs an efficient point-wise thresholding step.
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Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation

TL;DR: A way to approach the problem of dense optical flow estimation by integrating rich descriptors into the variational optical flow setting, while reaching out to new domains of motion analysis where the requirement of dense sampling in time is no longer satisfied is presented.
Proceedings ArticleDOI

DeepFlow: Large Displacement Optical Flow with Deep Matching

TL;DR: This work proposes a descriptor matching algorithm, tailored to the optical flow problem, that allows to boost performance on fast motions, and sets a new state-of-the-art on the MPI-Sintel dataset.
References
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Journal ArticleDOI

Nonlinear total variation based noise removal algorithms

TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.
Proceedings Article

An iterative image registration technique with an application to stereo vision

TL;DR: In this paper, the spatial intensity gradient of the images is used to find a good match using a type of Newton-Raphson iteration, which can be generalized to handle rotation, scaling and shearing.
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.
Journal ArticleDOI

Performance of optical flow techniques

TL;DR: These comparisons are primarily empirical, and concentrate on the accuracy, reliability, and density of the velocity measurements; they show that performance can differ significantly among the techniques the authors implemented.
Book

Computer Vision: Algorithms and Applications

TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
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