A brief tutorial/intro to the mathematical morphology in image processing.

Augmented reality technology grows rapidly for last two years. The huge potential this technology has not fully revealed. In the near future we expect appearance of large number of companies seeking to take a a new area in the market. More and more quality and exciting applications of augmented reality will appear. Want to know why?

Almost all image processing algorithms uses gray scale images as input source. But almost all hardware video sources provide frames in RGB/BGR(A) formats. So gray scale conversion is very popular operation. Although it’s expensive enough to cause CPU-bound bottlenecks while running on mobile processors. In this post i will show you how to use ARM NEON intrinsic to get significant performance boost of BGRA to GRAY conversion.

Hello everyone! Today i want to share my results in research of markerless augmented reality. The  main idea - do fast and quality AR without those damn markers and give the ability to use real object as a target. Markerless augmented reality is very similar to marker-based systems like ARToolkit with one major difference - such technology use real object as a target for augmentation. It can be almost any kind of objects - photos, logos, beer bottle or Cola can.

Last year I was tightly connected with image processing and feature tracking/matching. For my needs I’ve used SURF and later RIFF descriptors. Both of them have strong advantages and but… SURF descriptor robustness are compensated by it’s computational cost. RIFF descriptor extracts much faster but not robust enough for my needs. My needs are very simple – doing markerless AR on mobile phone.