文件列表:

sift-matlab\appendimages.m (359, 2006-11-15)
sift-matlab\demo-data\image1.JPG (14060, 2003-02-17)
sift-matlab\demo-data\image2.JPG (13579, 2003-02-17)
sift-matlab\demo-data\left.JPG (70281, 2013-11-26)
sift-matlab\demo-data\right.JPG (72421, 2013-11-26)
sift-matlab\demo-data\Thumbs.db (24064, 2014-09-25)
sift-matlab\demo-data\view01.png (578897, 2003-04-25)
sift-matlab\demo-data\view02.png (574557, 2003-04-25)
sift-matlab\descriptor\do_descriptor.m (5412, 2010-06-03)
sift-matlab\do_demo_1.m (950, 2015-01-23)
sift-matlab\do_demo_2.m (1157, 2015-01-23)
sift-matlab\do_demo_3.m (1173, 2015-01-23)
sift-matlab\do_demo_4.m (1168, 2015-01-23)
sift-matlab\do_descriptor.m (5412, 2010-06-03)
sift-matlab\do_diffofg.m (464, 2006-11-14)
sift-matlab\do_extrefine.m (4393, 2015-01-24)
sift-matlab\do_gaussian.m (3040, 2014-10-06)
sift-matlab\do_localmax.m (2144, 2014-10-06)
sift-matlab\do_match.m (5001, 2012-12-13)
sift-matlab\do_orientation.m (2509, 2015-01-27)
sift-matlab\do_sift.m (3959, 2015-01-23)
sift-matlab\imreadbw.m (301, 2006-11-16)
sift-matlab\key-location\do_extrefine.m (4322, 2010-06-03)
sift-matlab\key-location\do_localmax.m (2144, 2014-10-06)
sift-matlab\main.m (140, 2006-05-04)
sift-matlab\match\do_match.m (5001, 2012-12-13)
sift-matlab\orientation\do_orientation.m (2505, 2010-05-31)
sift-matlab\plotsiftframe.m (1812, 2010-06-03)
sift-matlab\plotss.m (640, 2006-11-20)
sift-matlab\scale-space\do_diffofg.m (464, 2006-11-14)
sift-matlab\scale-space\do_gaussian.m (3040, 2014-10-06)
sift-matlab\scale-space\smooth.m (215, 2006-11-14)
sift-matlab\sift_demo.m (1720, 2015-01-23)
sift-matlab\smooth.m (215, 2006-11-14)
sift-matlab\tightsubplot.m (1859, 2006-11-20)
sift-matlab\util\appendimages.m (359, 2006-11-15)
sift-matlab\util\imreadbw.m (301, 2006-11-16)
sift-matlab\util\plotsiftframe.m (1812, 2010-06-03)
sift-matlab\util\plotss.m (640, 2006-11-20)
... ...

SCALE INVARIANT FEATURE TRANSFORM IMPLEMENTATION YANTAO ZHENG, NOEMIE PHULPIN yantaozheng@gmail.com, S0600506@nus.edu.sg This is a MATLAB implementation of the SIFT keypoint detector and descriptor [1]. FUNCTIONS The implementation consists of the following functions. They are organized in the folders based on the functionality. These functions are self-contained and can be utilized independently. do_gaussian: generate Gaussian scale space of input image do_diffofg: generate Difference of Gaussian (DoG) scale space do_localmax: select local extrema as the potential keypoints do_extrefine: refine the keypoints by discarding the ones with low contrast and along an edge do_orientation: compute the orientation of a support region of keypoint do_descriptor: compute the descriptor of a keypoint based on image gradients. do_match: match two images based on the nearest neighbor principle and spatial consistency. do_sift: generate the SIFT descriptors for a given input image. It basically executes all the functions above. do_demo_2: demo_data中left.jpg和right.jpg关键点匹配之后的图像 do_demo_3: demo_data中image1.jpg和image2.jpg关键点匹配后的图像 do_demo_4: demo_data中view01.png和view02.png关键点匹配后的图像 RUN THE PROGRAM If the purpose of using this software is to compute SIFT descriptors of images, users only need to handle do_sift function. do_sift takes an image as input and generate keypoints and descriptors as output. Detailed structures about input/output can be found in the program header comments. There are several demo programs avaiable. They are the examples to run the SIFT programs. Execute the demos as below: From MATLAB prompt > do_demo_1 IMPORTANT PARAMETERS The performance of SIFT image matching depends on several parameters. In do_sift function, tuning the following parameters can achive different number of SIFT keypoints and descriptors; and therefore, different matching performance. The values are the default ones given by Lowe. S=3 ; Number of sub-levels per octave omin= -1 ; Starting octave number O = 4; Max octave level thresh = 0.04 / S / 2 ; Contrast response threshold r = 15 ; Edge response threshold NBP = 4 ; Number of spatial bins NBO = 8 ; Number of orientation bins [1] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," IJCV, vol. 2, no. 60, pp. 91 110, 2004.

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