PrivacyPolicyHasly

利用改进的经验模态分解方法检测QRS波群(The improved empirical mode decomposition method detection QRS complex group)

说明：  sift和surf特征点提取，然后进行配准，通过ransac进行提纯(Sift and surf feature points are extracted, then registered and purified by RANSAC)

2015年最新国外算法，基于加权视觉词典的人群密度估计(crowd entimatin by a weighted bag of visual word model)

一个很实用的GVF SNAKE程序，对于新手来说，可以通过代码的学习，掌握GVF SNAKE的意义和处理步骤(A very practical GVF SNAKE procedures, for the novice, it can be through the code learning, GVF SNAKE grasp the meaning and processing steps)

基于样图的纹理合成 效果很好的块匹配合成(Based Texture Synthesis from Samples effective synthesis of block-matching)

对变形的条纹图像进行傅立叶变换得到二维频谱图并提取基频(Fourier transform in deformation stripes image two-dimensional spectrum and extract the fundamental frequency )

用ssim方法对两幅图像进行比较的matlab源码( useful code of matlab for comparing of two pictures)

对于视频3D-DWT变换处理,嵌入水印图像。属于数字视频水印算法，包含水印嵌入算法以及水印提取算法。(3DDWT wavelet transform)

图像修复是对图像中破损区域进行信息填充，以减少图像破损所带来的信息损失的过程。 传统的图像修复方法需要依赖图像的具体结构来制定相应的修复方法，压缩感知理论的提出，使得可以利 用信号的稀疏性来对图像进行修复。基于K 奇异值分解（KSVD）与形态学成分分析（MCA，Morphological Component Analysis）的图像修复方法首先采用形态学成分分析方法对破损图像进行特征分析，将其分解 为结构部分和纹理部分；然后基于学习型字典KSVD分别对这两部分进行过完备字典训练；最后利用训练得 到的字典实现对破损图像的修复。相比于传统的图像修复方法，该方法具有适应性强、修复效果好等优点(Image inpainting is to fill the missing data in corrupted images and thus to reduce the information loss of damaged image. Traditional inpainting algorithms are dependent on specific structure of target images compressive sensing theory makes is possible to realized image inpainting with signal sparsity. This paper proposes a novel inpainting algorithm based on KSVD and MCA algorithm, which first decomposes the image into texture part and structure part, and then trains the two dictionaries for these two parts with KSVD and reconstructs the original image with these two trained dictionaries. Experiment indicates that the proposed algorithm is of better adaptability and performance as compared with traditional algorithms.)

基于马尔可夫随机场(MRF)、条件迭代算法ICM的图像分割源码(The MRF, the ICM-based image segmentation source code to source image segmentation based on the MRF ICM)