2020年12月3日 · 本文介绍基于马尔可夫随机场(MRF)的图像分割算法原理及实现过程，包括模型背景、图像分割流程、概率计算方法，并提供Matlab和Python代码示例。 从贝叶斯理论到马尔可夫随机场（MRF）--以图像分割为例

概率无向图模型 (probabilistic undirected graphical model)又称为 马尔科夫随机场 (Markov Random Field)，或者马尔科夫网络。 而有向图模型通常被称为信念网络 (belief network)或者 贝叶斯网络 (Bayesian network)。 对于这个我们要稍加区分。 有向图每个边都是有方向的，箭头所指的方向表示了这个随机变量的概率分布点，比如一个节点a到节点b的一个箭头，这个箭头就表明了节点b的概率由a所决定。 我们举个简单的例子 (例子来源于深度学习圣经 P342使用图描述模型 …

We present an adaptation of the recently proposed graph-shifts algorithm for labeling MRF problems from low-level vision. Graph-shifts is an energy minimization algorithm that does labeling by dynamically manipulating, or shifting, the parent-child relationships in a...

labels given observations, P(x|y) • Stereopsis, labels are depths (disparities) • Optical flow, labels are motion vectors • Restoration, labels are intensities (colors) – [Geman & Geman, 1984]

We present an adaptation of the recently proposed graph-shifts algo-rithm for labeling MRF problems from low-level vision. Graph-shifts is an en-ergy minimization algorithm that does labeling by dynamically manipulating, or shifting, the parent-child relationships in a hierarchical decomposition of the im-age.

•Markov Random Fields(MRF) – A kind of undirected graphical model •To model vision problems: – Low level: image restoration, segmentation, texture analysis… –High level: object recognition and matching(structure from motion, stereo matching)…

Abstract: This paper addresses the problem of self-validated labeling of Markov random fields (MRFs), namely to optimize an MRF with unknown number of labels. We present graduated graph cuts (GGC), a new technique that extends the binary s-t graph cut for self-validated labeling.

In this paper, we show how generic pixel-level binary MRF model can be solved in the superpixel space. As the main contribution of this paper, we show that a superpixel-level MRF can be derived from the pixel-level MRF by substi-tuting the superpixel representation of the pixelwise label into the original pixel-level MRF energy.

2010年11月8日 · We define a probabilistic description of a MRF labeling based on all input range images and then employ loopy belief propagation to solve this MRF, leading to a globally optimised integration...

2009年7月1日 · We present a novel method to reformulate the NP-hard, k-way graph partitioning problem as an approximate minimal s-t graph cut problem, for which a globally optimal solution is found in polynomial time. Each non-terminal vertex in the original graph is replaced by a set of ceil (log_2 (k)) new vertices.