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.
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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SIFT_Matlab.rar (31个子文件)
SIFT_YantaoNoemie
match
do_match.m 5KB
do_match.asv 5KB
README.txt 2KB
main.m 140B
sift_demo.m 2KB
orientation
do_orientation.m 2KB
do_demo_4.m 1KB
key-location
do_localmax.asv 2KB
do_localmax.m 2KB
do_extrefine.m 4KB
scale-space
do_gaussian.m 3KB
smooth.m 215B
do_diffofg.m 464B
descriptor
do_descriptor.m 5KB
do_demo_1.m 867B
do_sift.m 4KB
do_demo_2.m 1KB
demo-data
object0024.view01.png 565KB
image068.JPG 14KB
Thumbs.db 70KB
image069.JPG 13KB
object0024.view03.png 561KB
do_demo_3.m 1KB
util
plotsiftframe.m 2KB
plotss.m 640B
tightsubplot.m 2KB
appendimages.m 359B
plotsiftdescriptor.asv 3KB
imreadbw.m 301B
do_demo_2.asv 2KB
README.txt.bak 2KB
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