搜索资源列表
Application-SURF
- 基于surf的图像特征的提取,采集,配准已经图像融合拼接技术的研究-Image feature extraction surf-based acquisition, registration has been studied image fusion splicing technology
test
- 利用OpenCV实现两幅图像特征点描述配准问题-using OpenCV library to match two image s feature.
harris
- 基于harris角点检测的图像配准方法,角点作为图像上的特征点,包含有重要的信息。-Image registration method based on harris corner detection, corner as feature points on the image, contains important information.
3D_MATCHing_SIFT
- matlab环境下的三维点云配准程序,采用点特征进行配准-Three-dimensional point cloud registration program in matlab, registration using point features
ImageProcessing
- VC++功能较多的图像处理程序,实现的功能有:图像变换 图像配准 图像分割、图像编码、图像增强、图像复原、图像特征码提娶图像识别、运动检测、图像显示等,特别是源码里面的一些VC的图像处理类,你可以用到你自己的程序中。-VC++ function more image processing program, the functions are: image transform image registration image segmentation, image coding, image en
sift
- 提取SIFT特征点,配准拼接的代码,能运行-detect sift features
Image-pasting
- 相邻图像的配准及拼接是全景图生成技术的关键,有关图像配准技术的研究至今已有很长的历史,其主要的方法有以下两种:基于两幅图像的亮度差最小的方法和基于特征的方法。- Image pasting
image-matching-using-surf-and-ransac
- 对两幅图像进行配准,分别提取两幅图像的surf特征点以及描述子,得到粗匹配结果,然后根据粗匹配结果,采用ransac方法计算基础矩阵,并去除误匹配点,得到较准确匹配结果-Two image registration, surf was extracted from the feature points in two images to get the coarse matching and descr iptor, then according to the results, the coars
SIFT
- sift可对两幅图像进行对齐,配准,找出对应的特征点,画线,对进行拼接-Sift the two images aligned to registration, find out the corresponding feature points, line drawing, for stitching
harris
- 使用特征点来代表图像的内容,运动目标跟踪,物体识别,图像配准,全景图像拼接,三维重建-The use of feature points to represent the content of the image, moving object tracking, object recognition, image registration, image mosaics, 3D reconstruction
Image-registration-feature-points
- 特别全面的matlab数字图像处理经典案例 分别基于图像的特征点和特征区域进行图像配准-Particularly comprehensive digital image processing matlab classic case of the image based on feature points, respectively, and feature area image registration
PCL-1.6-sources
- PCL(Point Cloud Library)是在吸收了前人点云相关研究基础上建立起来的大型跨平台开源C++编程库,它实现了大量点云相关的通用算法和高效数据结构,涉及到点云获取、滤波、分割、配准、检索、特征提取、识别、追踪、曲面重建、可视化等。这里是PCL1.6的例子源码-PCL (Point Cloud Library) is on the basis of absorbing the predecessors Point Cloud related research to build a
Sift
- 一种尺度不变特征算子,应用于目标检测和图像配准。-image matching, a scale invariant operator
My_LBP
- LBP特征提取,根据学习资料自己编写的,用于做特征匹配和图像配准学习-a LBP features adoption program for image registration
boshi
- 基于Matlab的特征提取,图像配准系统的设计,项目完成,实现大场景,多光谱图像的配准。-Matlab-based feature extraction, image registration system design, project completion and achieve big scenes, multispectral image registration.
Registration-method4
- 基于特征的自适应正则化配准算法,摆脱了局部极小值的困扰,得到了正确的配准结果-Feature-based adaptive regularization registration algorithm, to get rid of the problems of local minima, and get the correct registration results
matching
- 本文主要致力于图像配准和拼接算法的研究,一方面以Harris算法为基础,提出了一种基于圆形邻域增强的角点配准算法,而另一方面则根据图像配准精度需求及庞大图像规模,将图像的拼接算法改进,提出基于尺度不变特征一种的图像拼接算法。-The thesis focuses on image registration and stitching algorithm, on the one hand to the Harris algorithm, proposed corner registration a
image-mosaic.doc
- 图像拼接(image mosaic)技术是将一组相互间重叠部分的图像序列进行空间匹配对准,经重采样合成后形成一幅包含各图像序列信息的宽视角场景的、完整的、高清晰的新图像的技术。图像拼接在摄影测量学、计算机视觉、遥感图像处理、医学图像分析、计算机图形学等领域有着广泛的应用价值。 一般来说,图像拼接的过程由图像获取,图像配准,图像合成三步骤组成,其中图像配准是整个图像拼接的基础。本文研究了两种图像配准算法:基于特征和基于变换域的图像配准算法。 在基于特征的配准算法的基础上,提出一种稳健的基于特征点的
FAST
- 对图像进行FAST特征点提取,对提取出的特征点计算互信息,以互信息为相似性度量进行图像配准-FAST the image feature point extraction, the extracted feature point mutual information to mutual information image registration as a similarity measure
ORB-Opencv
- 首先使用FAST对图像进行特征点提取,之后生成ORB描述符,进行点特征匹配,可以达到快速精确配准-The first to use the image FAST feature point extraction, after generating ORB descr iptors were point feature matching, can achieve fast and accurate registration