Wednesday, November 30, 2005
Psychedelic Fish Art
So I was trying to segment fish from a Jacques Cousteau video today (playing around with background subtraction, active contours, etc.)
...when I accidentally forgot to erase a window every frame, resulting in the following AI/Cousteau-generated art:
...when I accidentally forgot to erase a window every frame, resulting in the following AI/Cousteau-generated art:
Friday, November 11, 2005
Installing OpenCV beta5 (0.9.7) in Cygwin
These are revised instructions from:
http://www.dh.aist.go.jp/~kimura/opencv/opencv-0.9.7.html.en
First of all, install the Windows version of OpenCV 0.9.7 from the binaries on Sourceforge. Then, run this script from within an empty directory in cygwin:
opencv-cygwin-install.sh
That's it! You can try running the OpenCV examples (C:/Program Files/OpenCV/samples/c/*.exe) from a cygwin shell to make sure the DLLs were built properly.
http://www.dh.aist.go.jp/~kimura/opencv/opencv-0.9.7.html.en
First of all, install the Windows version of OpenCV 0.9.7 from the binaries on Sourceforge. Then, run this script from within an empty directory in cygwin:
opencv-cygwin-install.sh
That's it! You can try running the OpenCV examples (C:/Program Files/OpenCV/samples/c/*.exe) from a cygwin shell to make sure the DLLs were built properly.
Thursday, November 10, 2005
Shape Completion
Here is a set of six training shapes:
After computing the mean shape and performing PCA in the tangent space, the effects of the most significant eigenvectors (from highest to lowest) can be plotted (green shape is mean, blue shape is mean + 3*eigenvector):
Now if we input a partial shape, we can sample orientation and scale from the Bookstein matching between the mean shape and the partial shape (with Gaussian noise). For every orientation and scale sample we can compute the MLE completed shape with respect to our normal shape distribution (in the tangent plane).
A partial shape:
Some completed sample shapes:
And here is the completed shape with max. likelihood among all 50 samples:
After computing the mean shape and performing PCA in the tangent space, the effects of the most significant eigenvectors (from highest to lowest) can be plotted (green shape is mean, blue shape is mean + 3*eigenvector):
Now if we input a partial shape, we can sample orientation and scale from the Bookstein matching between the mean shape and the partial shape (with Gaussian noise). For every orientation and scale sample we can compute the MLE completed shape with respect to our normal shape distribution (in the tangent plane).
A partial shape:
Some completed sample shapes:
And here is the completed shape with max. likelihood among all 50 samples:
