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k-means+BOF

于 2020-11-28 发布 文件大小:11408KB
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下载积分: 1 下载次数: 14

代码说明:

  提取sift特征,通过K均值聚类形成特征包,进行图像检索。(SIFT features are extracted and image packets are retrieved through K mean clustering.)

文件列表:

k-means%2BBOF, 0 , 2018-05-14
k-means%2BBOF\do_database.m, 30791 , 2015-09-30
k-means%2BBOF\do_demo.m, 1517 , 2018-04-19
k-means%2BBOF\do_descriptor.m, 6482 , 2015-08-27
k-means%2BBOF\do_diffofg.m, 464 , 2012-09-27
k-means%2BBOF\do_eucidean_distance.m, 304 , 2016-04-13
k-means%2BBOF\do_extrefine.m, 4368 , 2012-11-05
k-means%2BBOF\do_gaussian.m, 3029 , 2012-10-26
k-means%2BBOF\do_localmax.m, 2261 , 2012-11-13
k-means%2BBOF\do_orientation.m, 2765 , 2015-08-22
k-means%2BBOF\do_sift.m, 4493 , 2015-10-09
k-means%2BBOF\get_countVectors.m, 676 , 2016-04-13
k-means%2BBOF\get_sifts.m, 713 , 2016-04-13
k-means%2BBOF\get_singleVector.m, 460 , 2016-04-13
k-means%2BBOF\img_paths.txt, 4447 , 2018-04-19
k-means%2BBOF\K_Means.m, 839 , 2016-04-13
k-means%2BBOF\SIFT_feature, 0 , 2018-05-14
k-means%2BBOF\SIFT_feature\._.DS_Store, 4096 , 2015-10-07
k-means%2BBOF\SIFT_feature\._do_database.m, 4096 , 2015-10-07
k-means%2BBOF\SIFT_feature\._do_descriptor.m, 4096 , 2015-10-07
k-means%2BBOF\SIFT_feature\._do_sift.m, 4096 , 2015-10-07
k-means%2BBOF\SIFT_feature\.DS_Store, 6148 , 2015-09-02
k-means%2BBOF\SIFT_feature\demo-data, 0 , 2018-05-14
k-means%2BBOF\SIFT_feature\demo-data\1.jpg, 5524 , 2012-10-17
k-means%2BBOF\SIFT_feature\demo-data\2.jpg, 5571 , 2012-10-17
k-means%2BBOF\SIFT_feature\demo-data\5.jpg, 35129 , 2012-10-17
k-means%2BBOF\SIFT_feature\demo-data\6.jpg, 34931 , 2012-10-17
k-means%2BBOF\SIFT_feature\demo-data\7.jpg, 9539 , 2012-10-17
k-means%2BBOF\SIFT_feature\demo-data\beaver11.bmp, 189956 , 2012-09-27
k-means%2BBOF\SIFT_feature\demo-data\beaver13.bmp, 189956 , 2012-09-27
k-means%2BBOF\SIFT_feature\demo-data\einstein.pgm, 65596 , 2012-08-15
k-means%2BBOF\SIFT_feature\demo-data\GML_RANSAC_Matlab_Toolbox_0[1].2.rar, 19215 , 2015-08-19
k-means%2BBOF\SIFT_feature\demo-data\harrisandransac.rar, 446099 , 2015-08-19
k-means%2BBOF\SIFT_feature\demo-data\image068.JPG, 14060 , 2012-09-27
k-means%2BBOF\SIFT_feature\demo-data\image069.JPG, 13579 , 2012-09-27
k-means%2BBOF\SIFT_feature\demo-data\image1.jpg, 240943 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image10.jpg, 63924 , 2015-08-21
k-means%2BBOF\SIFT_feature\demo-data\image11.jpg, 145849 , 2015-08-21
k-means%2BBOF\SIFT_feature\demo-data\image2.jpg, 393897 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image3.jpg, 613687 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image4.jpg, 659244 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image5.jpg, 403386 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image6.jpg, 36967 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image7.jpg, 48612 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image8.jpg, 92051 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\replace1.jpg, 2466289 , 2013-07-01
k-means%2BBOF\SIFT_feature\demo-data\replace2.jpg, 2812145 , 2013-07-01
k-means%2BBOF\SIFT_feature\demo-data\view01.png, 578897 , 2012-09-27
k-means%2BBOF\SIFT_feature\demo-data\view02.png, 574557 , 2012-09-27
k-means%2BBOF\SIFT_feature\do_database.m, 30791 , 2015-09-30
k-means%2BBOF\SIFT_feature\do_descriptor.m, 6482 , 2015-08-27
k-means%2BBOF\SIFT_feature\do_diffofg.m, 464 , 2012-09-27
k-means%2BBOF\SIFT_feature\do_extrefine.m, 4368 , 2012-11-05
k-means%2BBOF\SIFT_feature\do_gaussian.m, 3029 , 2012-10-26
k-means%2BBOF\SIFT_feature\do_localmax.m, 2261 , 2012-11-13
k-means%2BBOF\SIFT_feature\do_orientation.m, 2765 , 2015-08-22
k-means%2BBOF\SIFT_feature\do_sift.m, 4493 , 2015-10-09
k-means%2BBOF\SIFT_feature\smooth.m, 243 , 2012-11-13
k-means%2BBOF\SIFT_feature\util, 0 , 2018-05-14
k-means%2BBOF\SIFT_feature\util\appendimages.m, 359 , 2012-09-27
k-means%2BBOF\SIFT_feature\util\plotsiftframe.m, 1812 , 2012-09-27
k-means%2BBOF\SIFT_feature\util\plotss.m, 640 , 2015-07-31
k-means%2BBOF\SIFT_feature\util\tightsubplot.m, 1859 , 2012-09-27
k-means%2BBOF\smooth.m, 243 , 2012-11-13
k-means%2BBOF\sourcePictures, 0 , 2018-05-14
k-means%2BBOF\sourcePictures\1.jpg, 18138 , 2018-04-14
k-means%2BBOF\sourcePictures\10.jpg, 9506 , 2018-04-14
k-means%2BBOF\sourcePictures\100.jpg, 9568 , 2018-04-15
k-means%2BBOF\sourcePictures\101.jpg, 15883 , 2018-04-15
k-means%2BBOF\sourcePictures\102.jpg, 5979 , 2018-04-15
k-means%2BBOF\sourcePictures\103.jpg, 4686 , 2018-04-15
k-means%2BBOF\sourcePictures\104.jpg, 24421 , 2018-04-15
k-means%2BBOF\sourcePictures\105.jpg, 25652 , 2018-04-15
k-means%2BBOF\sourcePictures\106.jpg, 9463 , 2018-04-15
k-means%2BBOF\sourcePictures\107.jpg, 19874 , 2018-04-15
k-means%2BBOF\sourcePictures\108.jpg, 5267 , 2018-04-15
k-means%2BBOF\sourcePictures\109.jpg, 18393 , 2018-04-15
k-means%2BBOF\sourcePictures\11.jpg, 6031 , 2018-04-14
k-means%2BBOF\sourcePictures\110.jpg, 5664 , 2018-04-15
k-means%2BBOF\sourcePictures\12.jpg, 7202 , 2018-04-14
k-means%2BBOF\sourcePictures\13.jpg, 5459 , 2018-04-14
k-means%2BBOF\sourcePictures\14.jpg, 16511 , 2018-04-14
k-means%2BBOF\sourcePictures\15.jpg, 16722 , 2018-04-14
k-means%2BBOF\sourcePictures\16.jpg, 17399 , 2018-04-14
k-means%2BBOF\sourcePictures\17.jpg, 18570 , 2018-04-14
k-means%2BBOF\sourcePictures\18.jpg, 21290 , 2018-04-14
k-means%2BBOF\sourcePictures\19.jpg, 8726 , 2018-04-14
k-means%2BBOF\sourcePictures\2.jpg, 18123 , 2018-04-14
k-means%2BBOF\sourcePictures\20.jpg, 15315 , 2018-04-14
k-means%2BBOF\sourcePictures\21.jpg, 16620 , 2018-04-14
k-means%2BBOF\sourcePictures\22.jpg, 10571 , 2018-04-14
k-means%2BBOF\sourcePictures\23.jpg, 3279 , 2018-04-14
k-means%2BBOF\sourcePictures\24.jpg, 15179 , 2018-04-14
k-means%2BBOF\sourcePictures\25.jpg, 4237 , 2018-04-14
k-means%2BBOF\sourcePictures\26.jpg, 16937 , 2018-04-14
k-means%2BBOF\sourcePictures\27.jpg, 8714 , 2018-04-14
k-means%2BBOF\sourcePictures\28.jpg, 6136 , 2018-04-14
k-means%2BBOF\sourcePictures\29.jpg, 30527 , 2018-04-14
k-means%2BBOF\sourcePictures\3.jpg, 16845 , 2018-04-14
k-means%2BBOF\sourcePictures\30.jpg, 31940 , 2018-04-14

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发表评论

0 个回复

  • CWIMATIFUS
    关于图像融合的工具箱,从国外网站上找到的,很好用,可以作为对比实验时使用。包含一些需要处理的源图像。(On image fusion toolbox, from abroad to find the site, very good, and can be used as a comparative experiment. Contains a number of source images need to be addressed.)
    2008-04-12 11:18:50下载
    积分:1
  • myexcise1
    我做的课题的响应信号,做了响应的小波包能量分析,希望对大家有帮助(I do issue a response signal, so the response of the wavelet packet energy analysis, we want to help)
    2011-09-18 15:22:52下载
    积分:1
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    目的是改善影像质量,包括去除高频噪声与干扰,及影像边缘增强、线性增强以及去模糊等。分为低通滤波(平滑化)、高通滤波(锐化)和带通滤波。(The purpose is to improve the image quality, including the removal of high frequency noise and interference, and image edge enhancement, linear enhancement and deblurring. Into a low-pass filter (smoothing), high-pass filtering (sharpening) and bandpass filtering.)
    2015-03-30 17:39:21下载
    积分:1
  • opencv-otsu
    Opencv处理图像,用ostu法实现图像的二值化(Opencv image processing, using ostu method to achieve image binarization)
    2013-07-21 16:00:03下载
    积分:1
  • lable_parallel
    说明:  八值图像连通区域标记,为每个连通区域分配一个唯一的标号,处理后的图像按照从左到右,从上到下的顺序获得连续的标号(Binary image connected region eight marks, for each connected region a unique label distribution deal with in accordance with the image from left to right, from top to bottom order of access to continuous labeling)
    2008-10-24 20:20:11下载
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    摄象机标定的两步法修改程序代码,标定摄象机内外参数(Two-step camera calibration procedure to amend the code, both within and outside the parameters of camera calibration)
    2008-03-21 19:24:11下载
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  • AGCWD
    來自於2012年IEEE TIP的論文 Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution ,其功能為透過分析影像中的直方圖統計資訊,達到快速增強影像對比的結果。(From the 2012 IEEE TIP paper "Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution"which functions as a histogram of the image by analyzing statistical information, to quickly enhance image contrast.)
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    2020-12-21 19:29:07下载
    积分:1
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    说明:  基于相关跟踪的飞机识别和目标跟踪,matlab代码实现(Target tracking of aircraft can be achieved)
    2020-04-13 14:31:59下载
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  • bFilter
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