今后讨论班的一些走向 谨记之......
上周末决定，今后讨论班将针对CVPR 2014和SIAM Conference on IMAGING SCIENCE(2014) 进行进一步的挖掘。上级给我们刚刚布置了SIAM会议的日程、内容安排。看完之后，顿觉亚历山大啊。
。。。。。会是好会。会址更是博主心仪之地。
虽不能至。心向往之......
会议介绍里面有个词用的非常好：from nano-scale to the astronomical-scale 呵呵 从纳米级到天文级 好 尺度的概念 正是大BOSS的最爱.....
看看会议简单介绍 对如今图像科学领域内的一些应用方向有个认识。在science, medicine, and engineering.领域内，一些应用非常有价值 ：
reconstruction（重建）， enhancement（增强），segmentation（切割），analysis（分析）, registration（配准）， compression（压缩），representation（表示,Interested ）and tracking
of two and three dimensional images（二维三维图像跟踪，非常有商业价值）
所应用到的背景知识：mathematical, statistical, and computational methods领域内的知识，比方
transform and orthogonal series methods（变换与正交级数）, nonlinear optimization（非线性优化）, numerical linear algebra（数值线性代数）, integral equations（积分方程）， partial differential equations（偏微分方程）, Bayesian and other statistical inverse estimation
methods（贝叶斯与其他反预计方法）, operator theory（算子理论）, differential geometry（微分几何）, information theory（信息理论）, interpolation and approximation（插值与逼近）, inverse problems（反问题）, computer graphics and vision（计算机图形与计算机视觉）, stochastic processes（随机过程）, and others.（好全面的一个总结，通过这些list。一个图像科学领域的研究范围轮廓大概清晰地出来了。看来今后努力的方向，路挺长呢。。。。）
非常全面的了。说明这次会议所覆盖的应用领域有用价值大，所使用到的数学知识背景深。图像科学大有可为。
一个好的会议简单介绍，稍作改动，就能够作为一个学科的简单介绍了，假设写在某些大学专业的招生简章、院系设置上。顿时高大上的感觉就来了
一些感兴趣的文章或者Topic列表
1.Yi Ma.The Pursuit of Low-dimensional Structures in High-dimensional Data(在Youtube上有同名类似的讲座，Yi Ma的英文非常Frequently ,语速稍快)
2.Antonin Chambolle.Convex Representations for Imaging Problems.(尚无资源)
貌似是做变分问题的.....address several results on convex representations for variational problems in imaging such as image partitioning, Mumford-Shah segmentation or matching problems.
3.The Vicent Caselles Student Award 获得者Vincent Duval
Fine properties of the TVL1 and the TV-G models: Geometry Versus Oscillations
所基于的文章(互联网上有下载)
A comparative analysis of the TVL1 and the TV-G models
4.Prize Lecture
A. M. Bruckstein, D. L. Donoho, and M. Elad: "From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images," SIAM Review, Vol. 51, no. 1, pp. 34-81, 2009 (互联网有资源 可google)
A. M. Bruckstein, D. L. Donoho, and M. Elad: "From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images," SIAM Review, Vol. 51, no. 1, pp. 34-81, 2009 (互联网有资源 可google)
以下有关sparse representation 的。标蓝色的表示大的研究方向，详细presentation 用红色标出.非常多都是作者工作的前沿或总结 未必有release，有兴趣的能够搜做作者相关工作 先有个预备 我会尽力整理 把能找到的资源都放出来.
5.Recent Trends in Single Image Super-Resolution
a) Tomer Peleg. A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution
b) Hairong Qi. Beta Process Joint Dictionary Learning for Coupled Feature Spaces and its Application to Single Image Super-Resolution
6. Recent Advances in Optimization Techniques and Applications in Imaging Sciences
a) Raymond Chan. A Two-stage Image Segmentation Method Using a Convex Variant of the Mumford-Shah Model and Thresholding.Mumford-Shah
Model (不知道为什么 这个模型被大量提到 有心好好看看)
b) Xile Zhao.A New Convex Optimization Model for Multiplicative Noise and Blur Removal.
c) Wotao Yin.New Sparse Regularization Evolving ℓ1 Subgradient.
d) (领域内的大牛)John Wright.Learning Sparsely Used Dictionaries via Convex Optimization
7.Tensor Decompositions in Numerical Analysis,Optimization and Imaging
a).Anton Rodomanov. Approximation of Energies in Markov Random Fields and Their Representation in TT-format
8 Variational Approaches for Image Sequence Analysis and Reconstruction.会议里非常多文章提到了Variation，看来我知道的太少了
a) Hui Ji.Robust Video Restoration by Joint Sparse and Low Rank Matrix Approximation (看到纪辉的文章了)
9.Modern Imaging Models, High Order Methods And Applications
a).Andrea Bertozzi.Analysis and Design of Fast Graph Based Algorithms for High Dimensional Data.
b).Bibo Lu.High-order Geometrical Variational and PDE Methods for Noise Removal.
10.Image Denoising: Trends,Connections, and Limitations
a).Marcelo Bertalm´ıo. Denoising an Image by Denoising its Curvature Image（题目简单 不知内容该怎样？）
11.Inverse Scattering Problems in Imaging Science
a).Shuai Lu. Multiscale Analysis for Ill-posed Problem with Support Vector Approach（最终看到支持向量方法的应用了 看看）
12.Statistical Techniques on Riemannian Manifolds for Analysis of Imaging Data
a) Ian Dryde. High-dimensional Manifold Valued Data Analysis
b) Hongtu Zhu. SS-SPMs: Spatially Smoothing Statistical Parametric Maps for Ultra-High Dimensional Imaging Data
c) Dan Cheng.Statistical Peak Detection in Images
先这些吧 看的眼花.....有能力的童鞋接着总结....