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Optical flow modeling and computation

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TLDR
A survey of optical flow estimation classifying the main principles elaborated during this evolution, with a particular concern given to recent developments is proposed.
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This article is published in Computer Vision and Image Understanding.The article was published on 2015-05-01 and is currently open access. It has received 368 citations till now.

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Book ChapterDOI

Flows in Networks

TL;DR: This chapter sees how the simplex method simplifies when it is applied to a class of optimization problems that are known as “network flow models” and finds an optimal solution that is integer-valued.
Proceedings Article

Highly accurate optic flow computation with theoretically justified warping

TL;DR: 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.
Posted Content

Fast Optical Flow using Dense Inverse Search

TL;DR: In this paper, the Dense Inverse Search-based method (DIS) is proposed to find correspondences inspired by the inverse compositional image alignment proposed by Baker and Matthews in 2001.
Book ChapterDOI

Fast Optical Flow Using Dense Inverse Search

TL;DR: The Dense Inverse Search-based method (DIS) is the efficient search of correspondences inspired by the inverse compositional image alignment proposed by Baker and Matthews (2001, 2004), making DIS ideal for real-time applications.
Proceedings ArticleDOI

Learned Video Compression

TL;DR: This work presents a new algorithm for video coding, learned end-to-end for the low-latency mode, which outperforms all existing video codecs across nearly the entire bitrate range, and is the first ML-based method to do so.
References
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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Proceedings ArticleDOI

Histograms of oriented gradients for human detection

TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Journal ArticleDOI

Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Book

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers

TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Book

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Related Papers (5)
Frequently Asked Questions (13)
Q1. What have the authors contributed in "Optical flow modeling and computation: a survey" ?

In this paper, the authors introduced principles of optical flow modeling and computation, and classified the main methodological aspects of existing methods. 

Obstacle detection and avoidance are the main tasks investigatedfor general robot control in real environment exploiting optical flow [57,67,82]. 

The basis of graph cuts is the maxflow/min-cut problem consisting in finding the optimal path between two nodes in a directed graph, solvable by many algorithm in polynomial time [91,102]. 

Low-order polynomials are usually sufficient to model smooth motion fields, and their small number of parameters allows for efficient computation. 

Research on speeding up block matching include multi-scale search strategies [243], integral images [83] or search in trees[158], but the recent most spectacular contribution was achieved in [15,16] with the PatchMatch algorithm. 

The main undesirable effect produced by smoothing at coarse levels is the loss of small and rapidly moving objects in the final estimated flow field. 

When ground truth is available, two error measures are commonly used, namely the Angular Error (AE) and the Endpoint Error (EPE). 

The optimization of the data term with v fixed is efficiently performed by a thresholding scheme [280], and fixing w yields the Rudin– Osher–Fatemi model [210], optimized with the duality based algorithm of [54]. 

It is possible to model an undirected MRF structure as a directed graph by introducing two additional source and sink nodes, and then interpret the min-cut partition as a binary label segmentation of the MRF energy. 

To avoid the drawbacks of coarse-to-fine schemes of variational methods, an active research direction focuses on the design of computationally tractable discrete optimization methods for the large scale problem of optical flow. 

After convergence of the messages, they can be used to define the probability of assigning a given label to a node, and the label with the maximum probability is chosen. 

Another possibility is to penalize higher-order derivatives of the flow, as done in [241] for the second derivative, to favor piecewise affine flow fields. 

The related layered approach [233,234] achieving state-of-the-art results requires several hours to process a pair of 640 480 pixels, and even GPU-based implementation [244] can need up to an hour.