搜索资源列表
lm
- 各种对图像的分类和聚类的方法，kmeas、knn、pca等，还有几种数据处理中的窗函数-Variety of image classification and clustering methods, kmeas, knn, pca, etc., there are several data processing window function
Matlab-code
- It contains all matlab code for image processing applications
pca_exercise
- 这是一份介绍PCA在图像处理里面的例子，里面代码都有详细介绍，很有价值-This is a descr iption of the image processing inside PCA example, which codes are detailed, great value
DeepLearning-master
- 深度学习的概念源于人工神经网络的研究。含多隐层的多层感知器就是一种深度学习结构。深度学习通过组合低层特征形成更加抽象的高层表示属性类别或特征，以发现数据的分布式特征表示。[1] 深度学习的概念由Hinton等人于2006年提出。基于深信度网(DBN)提出非监督贪心逐层训练算法，为解决深层结构相关的优化难题带来希望，随后提出多层自动编码器深层结构。此外Lecun等人提出的卷积神经网络是第一个真正多层结构学习算法，它利用空间相对关系减少参数数目以提高训练性能。[1] 深度学习是机器
log
- 提出基于拉普拉斯高斯（Laplacian of Gaussian，LoG）算子边缘检测的全局二值化方法对其进行处理，该方法通过提取图像边缘部份的像素灰度获得图像二值化的阈值。处理结果表明，与传统的几种方法相比，该方法能够快速选取良好的二值化阈值，较好地区分目标和背景，在相当大模板宽度内图像二值化的结果都令人满意。-Is put forward based on the Laplacian of Gaussian (LoG) Laplacian of Gaussian, operator edge
K-meansbased-on-image-segmentation
- 基于K-means聚类算法的图像分割 代码及实验测试结果-K-means clustering algorithm based on image segmentation code and some experimental results
packet-wavelet
- This code use for image texture classification use packet wavelet first level
Change-Detection-Code
- 遥感影像变化检测经典算法(IR-MAD、MAD、CVA、PCA),另外进行了算法的Demo和精度等计算评价（OA、Kappa、AUC、ROC）-Remote sensing image change detection classical algorithm (IR-MAD, MAD, CVA, PCA), were additionally algorithms and calculation accuracy Demo Assessment (OA, Kappa, AUC, ROC)
K-means
- k-means简单实现，实现了k近邻的实现，以图像的形式显示出来，简单实用-k-means simple to achieve achieve a k neighbors realized and presented in the form of an image, simple and practical
DIGG-all
- 图生成器DEGG，能够自动生成各种属性的图，注意是graph，而不是image。-Map generator DEGG, can automatically generate various figures attributes, note the graph, rather than the image.
nmf
- 非负矩阵分解，处理合成孔径雷达图像，数据处理(The multichannel or wide-angle imaging performance of synthetic aperture radar (SAR) can be improved by applying the compressed sensing (CS) theory to each channel or sub-aperture image formation independently.)
eccv10_tutorial_part2
- 稀疏编码图像分类, 稀疏表示创始人写的PPT,内容精彩,分析清晰,易于理解(sparse coding image classification, PPT written by the original author of sparse representation, the PPT content is easy to realize with clear illustration and analysis.)