Topic
Markov random field
About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.
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23 Jun 2014
TL;DR: 3D information is exploited to automatically generate very accurate object segmentations given annotated 3D bounding boxes in a binary Markov random field which exploits appearance models, stereo and/or noisy point clouds, a repository of 3D CAD models as well as topological constraints.
Abstract: Labeling large-scale datasets with very accurate object segmentations is an elaborate task that requires a high degree of quality control and a budget of tens or hundreds of thousands of dollars. Thus, developing solutions that can automatically perform the labeling given only weak supervision is key to reduce this cost. In this paper, we show how to exploit 3D information to automatically generate very accurate object segmentations given annotated 3D bounding boxes. We formulate the problem as the one of inference in a binary Markov random field which exploits appearance models, stereo and/or noisy point clouds, a repository of 3D CAD models as well as topological constraints. We demonstrate the effectiveness of our approach in the context of autonomous driving, and show that we can segment cars with the accuracy of 86% intersection-over-union, performing as well as highly recommended MTurkers!
100 citations
17 Oct 2005
TL;DR: Experiments with natural and synthetic sequences illustrate how the learned optical flow prior quantitatively improves flow accuracy and how it captures the rich spatial structure found in natural scene motion.
Abstract: We develop a method for learning the spatial statistics of optical flow fields from a novel training database. Training flow fields are constructed using range images of natural scenes and 3D camera motions recovered from handheld and car-mounted video sequences. A detailed analysis of optical flow statistics in natural scenes is presented and machine learning methods are developed to learn a Markov random field model of optical flow. The prior probability of a flow field is formulated as a field-of-experts model that captures the higher order spatial statistics in overlapping patches and is trained using contrastive divergence. This new optical flow prior is compared with previous robust priors and is incorporated into a recent, accurate algorithm for dense optical flow computation. Experiments with natural and synthetic sequences illustrate how the learned optical flow prior quantitatively improves flow accuracy and how it captures the rich spatial structure found in natural scene motion.
99 citations
Posted Content•
TL;DR: This paper addresses semantic segmentation by incorporating high-order relations and mixture of label contexts into MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass.
Abstract: Semantic segmentation tasks can be well modeled by Markov Random Field (MRF). This paper addresses semantic segmentation by incorporating high-order relations and mixture of label contexts into MRF. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN to model unary terms and additional layers are devised to approximate the mean field (MF) algorithm for pairwise terms. It has several appealing properties. First, different from the recent works that required many iterations of MF during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing models as its special cases. Furthermore, pairwise terms in DPN provide a unified framework to encode rich contextual information in high-dimensional data, such as images and videos. Third, DPN makes MF easier to be parallelized and speeded up, thus enabling efficient inference. DPN is thoroughly evaluated on standard semantic image/video segmentation benchmarks, where a single DPN model yields state-of-the-art segmentation accuracies on PASCAL VOC 2012, Cityscapes dataset and CamVid dataset.
99 citations
TL;DR: This work studies estimation of M-matrices taking the role of inverse second moment or precision matrices using sign-constrained log-determinant divergence minimization, and proposes an algorithm based on block coordinate descent in which each sub-problem can be recast as non-negative least squares problem.
99 citations
16 Jun 1990
TL;DR: The Markov random field formalism is considered, a special case of the Bayesian approach, in which the probability distributions are specified by an energy function, and simple modifications to the energy can give a direct relation to robust statistics or can encourage hysteresis and nonmaximum suppression.
Abstract: An attempt is made to unify several approaches to image segmentation in early vision under a common framework. The energy function, or Markov random field, formalism is very attractive since it enables the assumptions used to be explicitly stated in the energy functions, and it can be extended to deal with many other problems in vision. It is shown that the specified discrete formulations for the energy function are closely related to the continuous formulation. When the mean field theory approach is used, several previous attempts to solve these energy functions are effectively equivalent. By varying the parameters of the energy functions, one can obtain a class of solutions and several nonlinear diffusion approaches to image segmentation, but it can be applied equally well to image or surface reconstruction (where the data are sparse). >
99 citations