Description: 遥感影像的自动配准是长期以来一直未能很好解决的一个重要问题。本文将视频图像匹配中获得巨大成功的SIFT( Scale Invariant Feature Transform)特征应用于遥感影像的自动配准问题中,并且针对遥感影像的成像特点,给出了一种具体的特征匹配方法。对航空和航天遥感影像在不同的变形、不同的光照变化和不同的分辨率下进行的大量实验表明,该方法具有稳定、可靠、快速等特点。-remote sensing images automatic registration is not very good for a long time have been resolved a key issue. Video images of this match was the great success sciences (Scale Invariant Feat ure Transform) of remote sensing images used in the automatic registration problems, remote sensing and imaging characteristics of the image is given a specific feature matching methods. For the aviation and aerospace remote sensing images in different deformation, changes in different light and different resolutions for the large number of experiments show that the method is stable, reliable, quick. Platform: |
Size: 1080726 |
Author:康康 |
Hits:
Description: 遥感影像的自动配准是长期以来一直未能很好解决的一个重要问题。本文将视频图像匹配中获得巨大成功的SIFT( Scale Invariant Feature Transform)特征应用于遥感影像的自动配准问题中,并且针对遥感影像的成像特点,给出了一种具体的特征匹配方法。对航空和航天遥感影像在不同的变形、不同的光照变化和不同的分辨率下进行的大量实验表明,该方法具有稳定、可靠、快速等特点。-remote sensing images automatic registration is not very good for a long time have been resolved a key issue. Video images of this match was the great success sciences (Scale Invariant Feat ure Transform) of remote sensing images used in the automatic registration problems, remote sensing and imaging characteristics of the image is given a specific feature matching methods. For the aviation and aerospace remote sensing images in different deformation, changes in different light and different resolutions for the large number of experiments show that the method is stable, reliable, quick. Platform: |
Size: 1080320 |
Author:康康 |
Hits:
Description: D.G lowe 的尺度不变特征转换算法（SIFT）
在基于opencv和OpenGL的源码基础上增加了图像的保存功能。用于对图像的尺度不变特征的提取以及图像特征点的匹配。-DG lowe the scale-invariant features of the conversion algorithm (SIFT) and OpenGL-based opencv source code based on the increase in the preservation of image features. Used to image the scale-invariant feature extraction and image feature points of the match. Platform: |
Size: 7247872 |
Author:朱 |
Hits:
Description: Invariant Local Feature for Image Matching,主要是介绍了一下图像副本检测的系统，先对图像提取sift特征，再用LSH匹配-Invariant Local Feature for Image Matching, mainly introduced a copy of what the image detection system, first sift the characteristics of the image extraction, and then match the LSH Platform: |
Size: 1699840 |
Author:潘自建 |
Hits:
Description: :提出一种基于尺度不变特征变换(SIFT)特征匹配的目标跟踪方法.首先使用SIFT提取目标特征,构
建目标特征库,然后使用基于K维树的特征匹配算法,对实时序列图像提取的SIFT特征与特征库中目标进行精确匹配
-: Based on Scale Invariant Feature Transform (SIFT) feature matching target tracking. The first to use SIFT target feature extraction, feature library building goals, and then use the K-dimensional tree based feature matching algorithm for real-time image sequences extracted SIFT features and characteristics of the target database accurately match Platform: |
Size: 264192 |
Author:LIDAN |
Hits:
Description: :研究了一种多目标识别算法,该算法用SUSAN角点形成SIFT特征点,采用阶梯图像金字塔结构实现尺度不变,为所有匹配点建立统一的超定线性方程组并对该方程组系数矩阵进行简
化使其维数降低一半,得到增广矩阵.对增广矩阵进行列变换,依据坐标转换的特性可从中提取多目标的稳定正常点,实现了快速分离多目标的匹配点.
-: Study of a multi-target recognition algorithm using SUSAN corner formed SIFT feature points, image pyramid structure by step to achieve scale-invariant, for all the match points to establish a unified super-set of linear equations and coefficients of the equations dimension matrix can be simplified to reduce by half, by the augmented matrix. on the augmented matrix column transformation, coordinate transformation based on the characteristics of multi-objective can be to extract the stability of the normal points, for a rapid separation of multi-target match point. Platform: |
Size: 241664 |
Author:LIDAN |
Hits:
Description: 利用图像的不变矩以及模式识别的内容，构造三维飞机图像的矩特征，然后再利用D-S证据理论的改进方法 吸收法对图像进行融合 对飞机的型号进行匹配-Moment invariant of image and pattern recognition of the content, structure features three-dimensional plane images of the moment, and then use the improved DS evidence theory method of absorption of the fusion images to match the type of aircraft Platform: |
Size: 359424 |
Author:陈虎 |
Hits:
Description: 本文在比较多种仿射不变性区域的基础上，选取最大稳定极值区域(MSER)对图像内容进行分割和提取，并通过构造仿射不变量的方法，对提取出来的区域进行规整化，进而从规整化的区域中提取SIFT描述子，然后根据匹配目标的需要进行特征的组合、变换，以形成易于匹配、稳定性好的特征向量，从而把图像匹配问题转化为特征的匹配问题 通过改进的K均值算法对特征向量进行聚类，聚类中心作为图像的视觉关键词:利用视觉关键词的思想，把文本检索技术领域的方法移植到图像检索应用上；并研究视觉关键词之间的空间约束关系。-Most relevant between images often exist perspective, dimension, rotating change and fuzzy, partial sheltering and complex background general affine transformation, and in many image processing and application needs a stable and can in different image can repeat detection feature to eliminate the influence of these changes, so it has good stability and repeatable degrees and can match the local invariant features become sex research hotspot. Platform: |
Size: 463872 |
Author:陈利华 |
Hits:
Description: 图像匹配的实现，其中match.m：测试程序，sift.m :尺度不变特征变换（SIFT算法）的核心算法程序，appendimages.m: 该函数创建一个新的图像分别包含两个匹配的图像和他们之间的匹配对的连接直线，可以运行。
-The realization of image matching, which match.m: test program, sift.m: scale-invariant feature transform (SIFT algorithm) of the core algorithm, appendimages.m: This function creates a new image each consists of two images and matching match between them on the connection line, you can run. Platform: |
Size: 2710528 |
Author:刘辉龙 |
Hits:
Description: SIFT特征（Scale-invariant feature transform，尺度不变特征转换)是一种电脑视觉的算法用来侦测与描述影像中的局部性特征，它在空间尺度中寻找极值点，并提取出其位置、尺度、旋转不变量，此算法由 David Lowe 在1999年所发表，2004年完善总结。其应用范围包含物体辨识、机器人地图感知与导航、影像缝合、3D模型建立、手势辨识、影像追踪和动作比对。-Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, and match moving. Platform: |
Size: 8782848 |
Author:张博 |
Hits:
Description: The SIFT algorithm[1] takes an image and transforms it into a collection of local feature vectors.
Each of these feature vectors is supposed to be distinctive and invariant to any scaling, rotation or
translation of the image. In the original implementation, these features can be used to find
distinctive objects in differerent images and the transform can be extended to match faces in
images. This report describes our own implementation of the SIFT algorithm and highlights
potential direction for future research.-The SIFT algorithm[1] takes an image and transforms it into a collection of local feature vectors.
Each of these feature vectors is supposed to be distinctive and invariant to any scaling, rotation or
translation of the image. In the original implementation, these features can be used to find
distinctive objects in differerent images and the transform can be extended to match faces in
images. This report describes our own implementation of the SIFT algorithm and highlights
potential direction for future research. Platform: |
Size: 357376 |
Author:bou33aza |
Hits:
Description: 图像特征匹配 巴西人写的，可以直接从图像进行模板匹配，算法对缩放和旋转具有不变性-image template match，wirtten by a Brazilian，this algorithm is invariant for scale and rotation of images Platform: |
Size: 3908608 |
Author:xing |
Hits:
Description: 基于局部不变特征的图像匹配。利用SIFT算法实现局部特征的检测与提取，再用RANSAC算法筛选特征点，提高正确的匹配率。-Based image matching local invariant features. Use SIFT algorithm to detect and local feature extraction, and then filtering feature points RANSAC algorithm to improve the correct match rate. Platform: |
Size: 131072 |
Author:朱迪 |
Hits:
Description: 正确的程序，求图像不变矩，根据匹配度进行分类-Correct procedures for image invariant moments, classified according to the degree of match Platform: |
Size: 7422976 |
Author:twh |
Hits:
Description: Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
The algorithm is patented in the US the owner is the University of British Columbia.-Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
The algorithm is patented in the US the owner is the University of British Columbia. Platform: |
Size: 90112 |
Author:dhivya |
Hits:
Description: Sift(Scale Invariant Feature Transform)是一个很好的图像匹配算法，同时能处理亮度、平移、旋转、尺度的变化，利用特征点来提取特征描述符，最后在特征描述符之间寻找匹配。本源码是用c语言实现的，代码简单易理解-Sift (Scale Invariant Feature Transform) is a good image matching algorithm, and can handle the brightness, pan, rotate, scale changes, the use of feature points extracted feature descriptor, and finally find a match between the characterization character. Platform: |
Size: 5197824 |
Author:huangxiaoli |
Hits:
Search - invariant match image