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imnoise
自己编的加噪声的 很好用哦(Own noise increases by Oh well)
- 2008-07-03 15:41:01下载
- 积分：1
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ADMM-MATLAB
基于ADMM的TV正则化最小化稀疏重建算法(TV Minimization Sparse Reconstruction Algorithms Based on ADMM)
- 2020-11-09 19:39:47下载
- 积分：1
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CT-tracking
一种简单高效地基于压缩感知的跟踪算法。首先利用符合压缩感知RIP条件的随机感知矩对多尺度图像特征进行降维，然后在降维后的特征上采用简单的朴素贝叶斯分类器进行分类。该跟踪算法非常简单，但是实验结果很鲁棒，速度大概能到达40帧/秒(A simple and efficient tracking algorithm based on compressed sensing. Firstly, with the random sensing matrix compressed sensing RIP conditions for multi-scale image feature dimension reduction, and then use the naive Bias classifier simple classification in the feature reduction after the. The tracking algorithm is very simple, but the results are robust, speed can reach 40 frames per second)
- 2014-01-10 11:45:54下载
- 积分：1
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wallis-filter-shadow-removal-master
说明： Wallis滤波可以有效去除影像的不均匀光照现象，因此使用Wallis滤波进行阴影去除算法(a kind of shadow removal algorithm which combining with Wallis filter)
- 2020-10-29 15:49:57下载
- 积分：1
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基于Retinex算法的单尺度图像增强算法 ssr1
说明： 基于Retinex算法的单尺度图像增强算法。(An image enhancement algorithm based on RETINEX theory.)
- 2020-06-23 04:40:02下载
- 积分：1
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opencv-otsu
Opencv处理图像，用ostu法实现图像的二值化(Opencv image processing, using ostu method to achieve image binarization)
- 2013-07-21 16:00:03下载
- 积分：1
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2
说明： <基于Matlab的数字图像相关法的程序实现>
数字图像相关法是对全场位移和应变进行分析的一种新的实验力学方法。图像处理是其核心之一。本文探讨了根据相关技术寻
求位移场的基本原理,并编制了基于Matlab的数字图像相关法程序。程序验证结果表明本程序处理速度快,精度高,可以满足基于相关技
术的图像处理的要求。(err)
- 2008-09-09 11:35:24下载
- 积分：1
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source-KALMMN
KALMAN filtering algorithm under the matlab toolbox, Shared by everybody, good source is very good
- 2017-05-14 16:12:25下载
- 积分：1
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数字图像处理_第三版_英_答案
说明： 《数字图像处理（第三版）》课后习题答案，作者冈萨雷斯(Answers to Exercises after Class of Digital Image Processing (Third Edition),author Gonzalez)
- 2019-06-10 10:54:09下载
- 积分：1
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demoBagSVM
一种基于半监督的svm的图像分类方法。该方法通过聚类核的方法利用无标记样本局部正则化训练核的表达式。这种方法通过图像直接学习一个自适应的核。该程序仿真的是文章：Semi-supervised Remote Sensing Image Classification with Cluster Kernels。大家可以参考下。(A semi-supervised SVM is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image, and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictionsds)
- 2013-09-03 10:44:56下载
- 积分：1
