PCA-SIFT

所属分类:matlab编程
开发工具:matlab
文件大小:668KB
下载次数:2196
上传日期:2007-06-20 16:40:48
上 传 者741704
说明:  结合PCA的尺度不变特征变换(SIFT)算法源代码,可用于图像目标检测和识别。
(combine PCA scale-invariant feature transformation (metabolism) algorithm source code, images can be used to target detection and identification.)

文件列表:
PCA-SIFT\pcasift-0.91nd\gpcavects.txt (1806948, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\Doxyfile (7843, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\Makefile.in (19361, 2004-06-06)
PCA-SIFT\pcasift-0.91nd\configure (142154, 2004-06-06)
PCA-SIFT\pcasift-0.91nd\AUTHORS (19, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\COPYING (1887, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\ChangeLog (0, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\INSTALL (9236, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\Makefile.am (713, 2004-06-06)
PCA-SIFT\pcasift-0.91nd\NEWS (0, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\aclocal.m4 (31962, 2004-06-06)
PCA-SIFT\pcasift-0.91nd\config.h.in (557, 2004-06-06)
PCA-SIFT\pcasift-0.91nd\configure.ac (573, 2004-03-22)
PCA-SIFT\pcasift-0.91nd\depcomp (12123, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\install-sh (5569, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\missing (10270, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\mkinstalldirs (1801, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\gathergrads.cc (1473, 2004-04-23)
PCA-SIFT\pcasift-0.91nd\keypoint.cc (18019, 2004-06-06)
PCA-SIFT\pcasift-0.91nd\keypoint.h (6915, 2004-04-23)
PCA-SIFT\pcasift-0.91nd\image.cc (10435, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\image.h (3224, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\getpatches.cc (921, 2004-04-23)
PCA-SIFT\pcasift-0.91nd\recalckeys.cc (795, 2004-01-27)
PCA-SIFT\pcasift-0.91nd (0, 2006-03-30)
PCA-SIFT\training\ploteigs.m (365, 2004-04-23)
PCA-SIFT\training\pca.m (559, 2004-04-23)
PCA-SIFT\training\pcag.m (627, 2004-04-23)
PCA-SIFT\training\ploteigs2.m (571, 2004-04-23)
PCA-SIFT\training\runpca.m (180, 2004-04-23)
PCA-SIFT\training\readeigs.m (227, 2004-04-23)
PCA-SIFT\training (0, 2006-03-30)
PCA-SIFT (0, 2007-06-20)

PCA-SIFT by Yan Ke This code implements the PCA-SIFT algorithm. Interest point detection code must be obtained separately. Any scale and orientation invariant interest point detector will work. Interest point representation is done through PCA gradient vectors. Given an image in PGM format, outputs keypoints in ascii text. The format is (mostly) compatible with David Lowe's keypoint format. Use mod_lowe_demoV2.tar.gz to detect keypoints and match results. Usage: ./keypoints < image1.pgm > image1.lkeys ./keypoints < image2.pgm > image2.lkeys ./recalckeys gpcavects.txt image1.pgm image1.lkeys image1.pkeys ./recalckeys gpcavects.txt image2.pgm image2.lkeys image2.pkeys ./match -im1 image1.pgm -k1 image1.pkeys -im2 image2.pgm -k2 image2.pkeys > out.pgm "keypoints" and "match" can be found in mod_lowe_demoV2.tar.gz For more information, visit: http://www.cs.cmu.edu/~yke/pcasift/

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