Preface .......................................... ix ......................................... Foreword xi ... .................................... The ECC web site xi11 ..................................... 1 Introduction .................... 1.1
In this article we study a continuous Primal-Dual method proposed by Appleton and Talbot and generalize it to other problems in image processing. We interpret it as an Arrow-Hurwicz method which leads to a better descr iption of the system of PDEs o
graphcut 入门 方法 经典文献 Fast Approximate Energy Minimization via Graph Cuts Interactive Graph Cuts for Optimal Boundary & Region Segmentation KBR-ICCV07Applications of parametric maxflow in computer vision Markov Random Fields with Efficient Approximatio
1 Information and Coding Theory 1 1.1 Information 3 1.1.1 A Measure of Information 3 1.2 Entropy and Information Rate 4 1.3 Extended DMSs 9 1.4 Channels and Mutual Information 10 1.4.1 Information Transmission over Discrete Channels 10 1.4.2 Informa
We propose a maximum a posteriori blind Poissonian images deconvolution approach with framelet regularization for the image and total variation (TV) regularization for the point spread function. Compared with the TV based methods, our algorithm not
对MCMC实现MIMOIn this paper, we develop novel Bayesian detection methods that are applicable to both synchronous code-division multiple-access and multiple-input multiple-output communication systems. Markov chain Monte Carlo (MCMC) simulation techniqu
用python写的一段贝叶斯网络的程序 This file describes a Bayes Net Toolkit that we will refer to now as BNT. This version is 0.1. Let's consider this code an "alpha" version that contains some useful functionality, but is not complete, and is not a ready-to-use "a
Abstract: Current approaches for visual-inertial odometry
(VIO) are able to attain highly accurate state estimation via
nonlinear optimization. However, real-time optimization quickly
becomes infeasible as the trajectory grows over time; this prob
Sumio Watanabe。高清原版PDF，已经裁边，适合阅读。用pdf xchange pro恢复裁剪的页面：依次点：左下角“选项”->“视图”->页面缩略图（快捷键是ctrl+T）。左侧面板中的缩略图，页面右键->裁剪页面（快捷键是ctrl+shift+T）。弹出的菜单中:“设为0”->（页码范围框中）选中“全部”->确定。Taylor Francis
Taylor Francis Group
http://taylorandfrancis.com
Math
Abstract— Visual-inertial SLAM (VI-SLAM) requires a good
initial estimation of the initial velocity, orientation with respect
to gravity and gyroscope and accelerometer biases. In this paper
we build on the initialization method proposed by Martinell
Mathematics for Machine Learning，作者是Marc Peter Deisenroth, A Aldo Faisal, Cheng Soon Ong
这本书的书签应该是正确的Contents
List of illustrations
Foreword
Part i Mathematical Foundations
1 Introduction and motivation
11
1.1 Finding Words for Intuitions
12
1.2 Two
In this paper, we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform (2D-SMCWT). The fusion of the detail 2D-SMCWT coefficients is performed via a Bayesian Maximum a Posteriori (MAP) approach by consi
We provide a review of our recent 100-Gb/s, high spectral efficiency (SE) experiment targeting transoceanic and regional undersea transmission distances. We demonstrated that simple pre-filtering at the transmitter together with a maximum a posterior
最大后验（Maximum A Posteriori，MAP）概率估计
注：阅读本文需要贝叶斯定理与最大似然估计的部分基础
最大后验（Maximum A Posteriori，MAP）估计可以利用经验数据获得对未观测量的点态估计。它与Fisher的最（极）大似然估计（Maximum Likelihood，ML）方法相近，不同的是它扩充了优化的目标函数，其中融合了预估计量的先验分布信息，所以最大后验估计可以看作是正则化（regularized）的最大似然估计。
想要了解最大后验（MAP）概率
