Week 07 - Blob blog.

0218

I spent the day revising my article for IHPC's newsletter. In my opinion, this is a timely checkpoint for a small self-assessment as well as a short break from development. The article is a brief overview on what Lightdraw is about and why it deserves attention (or not). Writing helps me see how much I've progressed thus far, and serves as a good gauge of how much of my work might actually be important enough to share. Furthermore, having to pen down the methodology in ink means having to explain the rationale of my code (how and when should the code be used). Without explanation code is just a bunch of logic and by itself is not very useful at all.



0219

Finally managed to finish the long overdue Lightdraw video with Peng and JL today.



0220

Favoured Dave Grossman's blob detection over my existing code which used contour detection. The blob detection is much more efficient since it picks up blobs in a single pass. As a result of the tremendous improvement of blob detection algorithm real-time laser tracking is possible.



0221

Studied my code closely, and with some trial and error I found a few pitfalls which reduced the frame rate.
- OpenCV's canny detection reduces frame rate by ~2 fps
- Frame rate is halved after I resized the OpenCV window.
- Morphology operations (i.e dilate, erode) reduces frame rate too. The larger the size of the structuring element used, the greater drop in fps.



0222

Since I'm so intrigued by blob detection efficiency over contour detection, I did some reading on their main differences. Discussion on performance difference between Counter detection and blob analysis and when to use which:
http://tech.groups.yahoo.com/group/OpenCV/messages/38670?threaded=1&m=e&var=1&tidx=1

In a nutshell, BlobsLib is faster for more than 5 blobs. Contour is faster for lesser blobs.
My suggestion is to take the advice with a pinch of salt and do your own benchmarking to see which works best for you.