搜索资源列表
zuidazuixiaofangfa
- 图像分割：最大最小法对灰度图像基于区域的阈值分割-image segmentation : minimization of the largest gray image on the threshold of regional division
GCv2p1
- GC_optimization - software for energy minimization with graph cuts Version 2.0 http://www.csd.uwo.ca/faculty/olga/software.html-GC_optimization-software for energy min imization with graph cuts Version 2.0 http : / / www.csd.uwo.ca / faculty / olga /
tvdenoise
- The Rudin-Osher-Fatemi total variation (TV) denoising technique poses the problem of denoising as a minimization problem
Fast_Global_Minimization_Active_Contour
- Author: Xavier Bresson (％ Last version: Aug 3, 2008 ％ For more information: X. Bresson and T.F. Chan, \"Fast Minimization of the Vectorial Total Variation Norm and Applications to Color Image Processing\", CAM Report 07-25
Fast_Color_Denoising_ROF
- Name: gmac = global minimization of the active contour model ％ Descr iption: see paper \"Fast Global Minimization of the Active Contour/Snake Model\" in JMIV07 ％ Author: Xavier Bresson (xbresson@math.ucla.edu) ％ Lastest version: 07-09-21
MinCutAlgorithm
- 这是自己用c#写的一段关于图的最小切的算法，主要参考了论文： 《An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision》 此文中的算法是目前比较优秀的最小切算法
maxflow_fast2
- Software for energy minimization with graph cuts。
SFS.rar
- Shape from shading: The source code for the review of shape from shading including minimization, local, propagation and linear methods,Shape from shading
l1_ls
- 基于最小二乘思想解决l1范最小问题，对于初学压缩感知的同学有一定的帮助 -L1 norm minimization problem based on least squares ideology, compressed sensing for the beginner students some help
SRC 实现了使用基于稀疏表示的人脸识别算法
- 该源码实现了使用基于稀疏表示的人脸识别算法。使用GPSR作为l1模最小化方法。-This pack of code implement a imges-based face recognition using sparse representation classification. In the algorithm, i employ GPSR as tool to complete the optimization procedure of l1-minimization.
L1
- 目前比较流行的稀疏分解重构程序，可以用在人脸识别、字典构造等方面。-Currently popular sparse decomposition and reconstruction process, can be used in face recognition, dictionary structure and so on.
nonrigid_version7b
- 非刚性图像配准算例，包括最速梯度下降优化、二次样条、2D/3D配准、互信息最小化、3D仿射等多种配准算法。 非刚性配准是当前应用最多的配准方法，用于处理有较大位移的配准问题-Non-rigid image registration examples, including the steepest gradient descent optimization, quadratic spline, 2D/3D registration, mutual information minimizatio
conjugategrads
- 图像重建常常被转化为解非线性无约束极值问题, 通过范数极小化推导出共扼梯度法的 一般算法。通过对模拟数据和实际工件断层扫描数据进行图像重建, 估计了算法的有效性, 结果表明, 与最速下降法相比, 此算法更适用于不完全投影数据的图像重建, 在保证重建图像拟合度的同时, 大大提高了重建速度。-Image reconstruction has often been transformed into solving nonlinear unconstrained extremum problem,
CodeAComparativeStudyofEnergyMinimizationMethods.r
- 是文章Code A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors的完全代码实现-it is the implementation of paper:Code A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness
matcher
- Dense Matching using energy minimization
l1magic
- This package contains code for solving seven optimization problems. -The main directory contains MATLAB m-files which contain simple examples for each of the recovery problems. They illustrate how the code should be used (it is fairly straightfor
Minimization_of_Energy_Image_Segmentation
- 基于可变区域能量最小化拟合的图像分割方法的图像分割-Variable region-based energy minimization method of fitting the image segmentation of the image segmentation
20090914
- 基于图割的能量最小化演示文稿。本人整理的，进一步理解图割理论的知识-Based on graph cut energy minimization of the presentation. I am finishing, and further understanding of the theory of knowledge in cutting plan
LBF_v0_v0.1
- 图像分割，Minimization of Region-Scalable Fitting Energy for Image Segmentation-LBF_v0.1: This code implements an improved algorithm slightly modified from the original LBF model in the above paper. A desirable advantage of this improved version o
Image_Segmentation_Active_Coutour_Local_Binary_Fit
- 李纯明最新实现的局部活动轮廓模型的图像分割，比CV模型方法好很多。包含所实现的论文Minimization of Region-Scalable Fitting Energy for Image Segmentation-Li Chunming latest realization of the local active contour model for image segmentation, much better than the CV model approach. The paper