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09特征匹配

于 2019-07-02 发布

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下载积分: 1 下载次数: 2

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说明： SIFT特征点检测 DoG金字塔图像生成后，通过全搜索中间层DoG图像(去除第一个和最后一个DoG图像)的像素点，判断当前像素点的像素值是否大于(或者小于)周围8邻域的像素值(sddsdadadsda da da d ad a da adad ad a d ad a d ad a d ad a d)

文件列表：

09特征匹配\01角点检测\Harris角点检测.doc, 586048 , 2019-07-02
09特征匹配\01角点检测\Harris角点检测.py, 1230 , 2019-07-02
09特征匹配\01角点检测\Shi-Tomasi角点检测.doc, 445224 , 2019-07-02
09特征匹配\01角点检测\Shi-Tomasi角点检测.py, 1125 , 2019-07-02
09特征匹配\02SIFT和DoG\SIFT.py, 739 , 2019-07-02
09特征匹配\02SIFT和DoG\使用DoG和SIFT进行特征提取与描述.doc, 1668678 , 2019-07-02
09特征匹配\03ORB特征匹配\ORB特征匹配.doc, 1125198 , 2019-07-02
09特征匹配\03ORB特征匹配\ORB特征匹配.py, 1084 , 2019-06-17
09特征匹配\04K-最近邻匹配\K-最近邻匹配.py, 882 , 2019-06-17
09特征匹配\04K-最近邻匹配\最近邻匹配.doc, 1068882 , 2019-07-02
09特征匹配\05Brute-Force匹配\B-F暴力匹配.doc, 580621 , 2019-07-02
09特征匹配\05Brute-Force匹配\暴力匹配.py, 1942 , 2019-06-13
09特征匹配\06FLANN\FLANN匹配.doc, 761120 , 2019-07-02
09特征匹配\06FLANN\FLANN匹配.py, 2107 , 2019-07-02
09特征匹配\images\chess_board.png, 71276 , 2019-04-27
09特征匹配\images\manowar_logo.png, 3769 , 2019-04-27
09特征匹配\images\manowar_single.jpg, 193347 , 2019-04-27
09特征匹配\images\varese.jpg, 174954 , 2019-04-27
09特征匹配\01角点检测, 0 , 2019-07-02
09特征匹配\02SIFT和DoG, 0 , 2019-07-02
09特征匹配\03ORB特征匹配, 0 , 2019-07-02
09特征匹配\04K-最近邻匹配, 0 , 2019-07-02
09特征匹配\05Brute-Force匹配, 0 , 2019-07-02
09特征匹配\06FLANN, 0 , 2019-07-02
09特征匹配\images, 0 , 2019-06-12
09特征匹配, 0 , 2019-07-02

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Lab1
1. There are at least 10 nodes in the simulation and each node has at least two neighbors within its transmission area. 2. Every node periodically broadcasts a ”request” packet to its neighbors. Meantime, this node sets a timer with t time interval to wait for its neighbors ”re- sponse” packets. When receiving all responses from its neighbors during t, it prints ”success”; oth- erwise, it prints ”failure”. Assume that the broadcast periodical interval is bigger than t. 3. A neighbor that receives a ”request” packet, sends a ”response” packet after a random time τ , τ = random(0, 2t).(There are at least 10 nodes in the simulation and each node has at least two neighbors within its transmission area. Every node periodically broadcasts a request packet to its neighbors. Meantime, this node sets a timer with t time interval to wait for its neighbors response packets. When receiving all responses from its neighbors during t, it prints success oth- erwise, it prints failure. Assume that the broadcast periodical interval is bigger than t.)

2018-01-30 10:02:40下载

积分：1
python http 客户端
python http 客户端实现文件或者文本上传下载

2019-10-29下载

积分：1
LSH
利用LSH算法在图片数据库中搜索与目标图片最相似的图片.hash思想在图像检索种的应用。(LSH picture database using the search algorithm with the target image most similar images. Hash kind of thinking in image retrieval applications.)

2020-12-09 19:09:19下载

积分：1
server 1.4
开启本地HTTP服务，并可以自定义端口，任意文件下载(Open the local HTTP service, and you can customize the port and download any file.)

2020-06-24 18:20:01下载

积分：1
lena
说明： SLIC算法是simple linear iterative cluster的简称，该算法用来生成超像素（superpixel）算法步骤：已知一副图像大小M*N,可以从RGB空间转换为LAB空间，LAB颜色空间表现的颜色更全面假如预定义参数K，K为预生成的超像素数量，即预计将M*N大小的图像(像素数目即为M*N)分隔为K个超像素块，每个超像素块范围大小包含[（M*N）/K]个像素假设每个超像素区域长和宽都均匀分布的话，那么每个超像素块的长和宽均可定义为S，S=sqrt(M*N/K) 遍历操作，将每个像素块的中心点的坐标(x,y)及其lab的值保存起来，加入到事先定义好的集合中(Algorithmic steps: Given the size of an image M*N, it can be converted from RGB space to LAB space, and the color space of LAB is more comprehensive. If the predefined parameter K, K is the number of pre-generated super-pixels, i.e., the image of M*N size (the number of pixels is M*N) is expected to be divided into K super-pixel blocks, each of which contains [(M*N)/K] pixels. Assuming that the length and width of each super-pixel region are evenly distributed, the length and width of each super-pixel block can be defined as S, S = sqrt (M*N/K). The traversal operation saves the coordinates (x, y) and lab values of the central points of each pixel block and adds them to a pre-defined set.)

2019-06-10 14:48:52下载

积分：1
123
说明：读取rinex n文件，并计算各时间卫星坐标位置(Read the RINEX n file and calculate the coordinates of each time satellite)

2019-10-22 22:11:20下载

积分：1
获取视频帧的两种方法
提供两种抽取视频帧的方法，一种为逐帧遍历，输出所需要的关键帧，另一种为预先设置帧号，直接从视频中抽取，两种方法适用场合不同，各有长处。

2022-08-09 13:58:24下载

积分：1
复杂网络K-shell法顶点排序实例.py
复杂网络K-shell法顶点排序实例.py

2020-11-07下载

积分：1
knn
说明： KNN的四种实现代码与数据，可直接运行....(code and data of knn algorithm , can be run directly)

2020-09-27 17:18:06下载

积分：1
1-Python
Python Essential Reference, Second Edition

2020-06-24 17:40:02下载

积分：1

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