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Description: DUP2用法-DUP2 usage
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Size: 17079 |
Author: 风行者 |
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Description: DUP2用法-DUP2 usage
Platform: |
Size: 16384 |
Author: 风行者 |
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Description: Unix课程作业。
使用fork(), exec(), dup2(), pipe() ，open()系统调用完成与下列shell命令等价的功能。
grep –v usr < /etc/passwd | wc –l > result.txt
-An assignment in UNIX course.
Using fork(), exec(), dup2(), pipe(), open() system calls to do the same function of the shell command below: grep –v usr < /etc/passwd | wc –l > result.txt
Platform: |
Size: 4096 |
Author: kank |
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Description: write a my own shell to execute all kinds of functions, clear, delete, exit , extract, file and so on. the detail info is in the requirements of the assignment. the source is consisted of parse command line arguments, execute all kinds of functions, using functions such as dup2, fork(), execvp(), execlp().
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Size: 19456 |
Author: sanmao |
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Description: dup2,dup函数使用
copy文件描述，然后回滚初始化状态-How to use the two func:dup,dup2?
Platform: |
Size: 1024 |
Author: tom |
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Description: We can group the conclusions based on a level of compression and the probe sets into two
parts: i) higher compression rates (0.5, 0.3 and in some cases even 0.2 bpp) seem to be
suitable for recognizing faces with different expressions (
fb probe set) and images taken in
different illumination conditions (fc probe set) ii) lower compression rates (1 bpp) seem to
be suitable for recognizing images taken at different points in time (
dup1 and dup2 probe
set). Taking this analysis into account, it seems that the current practice of deciding on the
level of compression based on visual distortion of images is wrong. While the images
compressed to 0.3 bpp are visually significantly distorted, the recognition results are in
almost all experiments statistically indistinguishable from the results achieved by using
uncompressed images. In many cases these results are slightly better and in some cases even
significantly better than the ones achieved with uncompressed images.-We can group the conclusions based on a level of compression and the probe sets into two
parts: i) higher compression rates (0.5, 0.3 and in some cases even 0.2 bpp) seem to be
suitable for recognizing faces with different expressions (
fb probe set) and images taken in
different illumination conditions (fc probe set) ii) lower compression rates (1 bpp) seem to
be suitable for recognizing images taken at different points in time (
dup1 and dup2 probe
set). Taking this analysis into account, it seems that the current practice of deciding on the
level of compression based on visual distortion of images is wrong. While the images
compressed to 0.3 bpp are visually significantly distorted, the recognition results are in
almost all experiments statistically indistinguishable from the results achieved by using
uncompressed images. In many cases these results are slightly better and in some cases even
significantly better than the ones achieved with uncompressed images.
Platform: |
Size: 15268864 |
Author: nahla |
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Description:
This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the filenames.
pca.m and createDistMat.m can be used on any database following the
same principles described in the header of the files. feret.m is specific
for the FERET database but can easily be transformed to be generic if needed.
In addition to the three .m files, standard FERET gallery and probe set lists
are given, along with a list of randomly chosen 500 images that can be used
for testing:
Training set: trainList.mat
Gallery: feretGallery.mat
Probe sets: fb.mat fc.mat dup1.mat dup2.mat-
This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the filenames.
pca.m and createDistMat.m can be used on any database following the
same principles described in the header of the files. feret.m is specific
for the FERET database but can easily be transformed to be generic if needed.
In addition to the three .m files, standard FERET gallery and probe set lists
are given, along with a list of randomly chosen 500 images that can be used
for testing:
Training set: trainList.mat
Gallery: feretGallery.mat
Probe sets: fb.mat fc.mat dup1.mat dup2.mat
Platform: |
Size: 34816 |
Author: harish bsv |
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