搜索资源列表
sift-match
- SIFT特征点检测，配准、匹配，代码经验证可用-sift match ransac appendimages
image-matching
- 影像匹配，针对左片和右片的影像匹配。具体流程：先建立影像金字塔，然后利用Moravec算子在影像上提取特征点，然后用相关系数作为评价准则，根据这些提取的特征点进行影像匹配-image matching, for the left image and right image. detailed process: first create an image pyramid, then extract feature points from the images with Moravec operat
panorama2
- 图像融合，SIFT算子，特征点的提取、匹配与融合-image stitching，sift
matching
- 基于核线约束的最小二乘匹配，可以提取特征点-least squares matching
SIFT_VC.rar
- sift算法，支持特征点提取，kdtree算法匹配的程序。,sift algorithm, program support feature point extraction, kdtree algorithm match.
SSD-and-SAD
- 用相似性方法做的特征点的匹配，比较了SSD与SAD方法的各自效果。-feature points matching method used by the similarity, and comparing the respective effects of the SSD and SAD methods.
RANSAC
- 提纯检测到的特征点的一个算法，可以和SIFT算法结合使用，去除一些误匹配的关键点，提高代码的执行效率-Purified feature points detected an algorithm that can be used in conjunction with SIFT algorithm to remove some of the mismatching of the key points to improve the efficiency of the implementation o
test
- 双目视觉的立体匹配和特征点检测，sift/surf/orb等特征都可以，ransac方法去噪-Stereo matching and feature points detection, sift/surf/ORB and other characteristics of the binocular vision can RANSAC method de-noising
pipei
- 首先利用HARRIS算子进行特征点提取，再进行基于相关系数的影像匹配，并输出TXT文件。-failed to translate
IMAGEPIPEI
- 首先利用HARRIS算子进行特征点提取，再进行基于相关系数的影像匹配，并输出TXT文件。-failed to translate
siftDemoV4
- 图像提取特征点，基于128位向量特征匹配，-Image extraction of feature points based on 128-bit vector feature matching,
imageStitching
- 图像拼接。首先采用surf算法提取特征点，再用双向匹配剔除误匹配，最后采用渐进渐出实现全景图-Image stitching. First feature points are extracted using surf algorithm, and then removed two-way matching mismatch, finally adopted gradually progressive realization panorama
image-retrieval
- 发表在ieee上的文章，介绍基于特征点的图像匹配方法，适合学习图像匹配的同学学习。-Articles published in the ieee, image matching algorithm based on feature points is presented in this paper, suitable for image matching students learning.
imagematchsourcecode.tar
- 立体匹配算法，里面的匹配算法不仅匹配的很准确，而且匹配到的特征点比大多数算法更多。是一个很难得的匹配算法源码！-Stereo matching algorithm, which matches the matching algorithm is not only very accurate, but matched feature points more than most algorithms. Is a very rare matching algorithm source code!
The-wavelet-transform
- 利用小波变换来提取特征点，主要是通过小波系数的比较，提取关键点，再通过关键点匹配，实现图像配准。-Using wavelet transform to extract feature points, mainly through the comparison of wavelet coefficients, extract key points, and then through the critical point matching, to achieve image registration
featurepoint
- 这是基于特征点的快速匹配算法，内容详细，值得参考！-This is based on feature points matching algorithm fast, detailed and worth considering!
RANSAC
- 对图像的特征点，进行匹配，利用RANSC找到最佳的映射矩阵-The image feature points matching, using the mapping matrix to find the best RANSC
matching
- 使用Harris Corner提取特征点，通过相关算法得到匹配结果后，再使用Ransac算法剔除错误匹配-Using Harris Corner feature point extraction, through the relevant algorithm matches, then use Ransac algorithms eliminate false matches
surfcheck
- 用surf算法完成了图像特征点的检测盒特征点的匹配-Completed with surf algorithm cartridge of the image feature points matching feature points
SIFT
- 用SIFT算法实现了刚性和非刚性图像的配准 并且效果非常理想 SIFT的特征点的提取 描述 和匹配的算法有注释-Nonrigid Registration Using Free-Form Deformation.