PCA-SIFT
所属分类：图形图像处理
开发工具：matlab
文件大小：668KB
下载次数：42
上传日期：2013-04-04 13:23:55
上 传 者：
dingcheng123
说明： PCA——SIFT 特征值提取算法实现 用于图形图像处理的sift特征提取
(PCA- SIFT the eigenvalue extraction algorithms to achieve)
文件列表:
PCA-SIFT\pcasift-0.91nd\aclocal.m4 (31962, 2004-06-06)
PCA-SIFT\pcasift-0.91nd\AUTHORS (19, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\ChangeLog (0, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\config.h.in (557, 2004-06-06)
PCA-SIFT\pcasift-0.91nd\configure (142154, 2004-06-06)
PCA-SIFT\pcasift-0.91nd\configure.ac (573, 2004-03-22)
PCA-SIFT\pcasift-0.91nd\COPYING (1887, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\depcomp (12123, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\Doxyfile (7843, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\gathergrads.cc (1473, 2004-04-23)
PCA-SIFT\pcasift-0.91nd\getpatches.cc (921, 2004-04-23)
PCA-SIFT\pcasift-0.91nd\gpcavects.txt (1806948, 2004-01-27)
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\INSTALL (9236, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\install-sh (5569, 2004-01-27)
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\Makefile.am (713, 2004-06-06)
PCA-SIFT\pcasift-0.91nd\Makefile.in (19361, 2004-06-06)
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\NEWS (0, 2004-01-27)
PCA-SIFT\pcasift-0.91nd\recalckeys.cc (795, 2004-01-27)
PCA-SIFT\training\pca.m (559, 2004-04-23)
PCA-SIFT\training\pcag.m (627, 2004-04-23)
PCA-SIFT\training\ploteigs.m (365, 2004-04-23)
PCA-SIFT\training\ploteigs2.m (571, 2004-04-23)
PCA-SIFT\training\readeigs.m (227, 2004-04-23)
PCA-SIFT\training\runpca.m (180, 2004-04-23)
PCA-SIFT\pcasift-0.91nd (0, 2013-03-12)
PCA-SIFT\training (0, 2013-03-12)
PCA-SIFT (0, 2013-03-12)
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/
近期下载者：
相关文件：
收藏者：