Week 06 - Integration with Laser Detection

0211

For simplicity's sake, development for the past few weeks was done in an ideal lighting environment. This week my objective is to move my setup to the back projection system.

First thing I needed was to get the camera to see the laser points at the very least. I felt that this is crucial, because even a perfect laser detection program would not be able to function properly if the video input does not reflect the laser point half the time. The best a program can do is to cope with lossy input and perhaps extrapolate a set of points when there are gaps in a stroke. Still, this requires reliable input from the camera at the frontline in order to provide for small loss tolerance levels.

After trying several kinds of configuration again, my conclusion is that placing the camera at the back with the projectors is ideal. Somehow our previous experiment (ref: post 0118) went wrong. Also, setting the exposure settings to lowest on the DVcam allows me to see only the laser dot, which is excellent really, but the bandwidth between the cam and the computer is slow at only ~15fps. [0218, Edit: I found a setting that enables me to jack up shutter speed using the sports mode, under the menu "Program AE". Joy!] An ideal camera would probably be one with manual exposure and manual focus and manual shutter speed settings that achieves >= 30fps easily.



0212

My school's academic supervisor Dr Teo came down for a visit today to learn what Lightdraw is about.

Demonstration went relatively well, except when I had to switch from the front camera to the one at the back. Mental note: For demonstration use only lightdraw account.

So far I am able to give the coordinate of the laser point on an image, detect its colour and trace its motion. Things get trickier when I have two different coloured lasers crossing paths, then I am not able to tell which path belongs to which, for now.



0213

Learned how to install drivers on Mac computers, and got my USB camera to work with the Light the Mac, but only on lightdraw account. Weird huh.

Debugging ensues...



0214

Today was spent cleaning up my code and trying to make it more efficient. Each time a image processing function is called, it makes a few passes (scanning every pixel of image). Furthermore, every loop calls a few of these functions, causing the program not being able keep up with the frame rate.

I found some articles and samples on blob detection mainly for comparison and while these algorithms highlights all kinds of blobs in general, some are really fast and efficient. Since I want to highlight laser points specifically, I could use some of the readily available blob detection methods and combine it with my own filters, then hopefully I would have something that is both efficient and reliable. Here's an interesting article I found on the comparison between blob analysis and edge detection algorithms in practice: http://archive.evaluationengineering.com/archive/articles/0806/0806blob_analysis.asp
Note: I found the article perspective is biased for edge detection algorithm.

OpenCV provides for blob detection, but the OpenCV community attests to its poor reliability.
Currently I am using another slightly different version (just a difference in structuring elements used) of blob detection that works fine for me, as I have cleaned up the image before feeding it to the blob analysis algorithm. The version of blob analysis that I am using is Dave Grossman's, who claimed that he implemented G J Agin's algorithm from memory after attending the latter's presentation. This is because Agin's paper is difficult to find since it is at least 35 years old! (I even tried looking in ACM and IEEE) Dave Grossman tries to explain the algorithm briefly in his post here http://osdir.com/ml/lib.opencv/2005-11/msg00200.html

More details on the algorithm here:
http://opencvlibrary.sourceforge.net/cvBlobsLib



0215

Got part of moveresize to work with laser on back projection screen. I can drag the box around now if I move my pointer around, slowly. Looks like there's more optimizing to be done.