Some phase correlation tricks

Then doing phase correlation on low-resolution, or extreme low-resolution (like below 32×32) images, the noise could become a serious problem, up to making result completely useless. Fortunately there are some tricks, which help in this situation. Some of them I stumbled upon myself, and some picked up in relevant papers.
First is obvious – pass image through the smoothing filter. Pretty simple window filter from integral image can help here.
Second – check consistency of result. Histogram of cross-power specter can help here. Here there is the wheel within the wheel, which I have found out the hard way – discard lower and right sectors of cross-power specter for histogram, they are produced from high-frequency parts of the specter and almost always are noise, even if cross-power specter itself quite sane.
Now more academic tricks:
You could extract sub-pixel information from cross-power specter. There are lot of ways to do it, just google/citeseer for it. Some are fast and unreliable, some slow and reliable.
Last one is really nice, I’ve picked it from Carneiro & Japson paper about phase-based features.
For cross power specter calculation instead of

use

where is a small positive parameter
This way harmonics with small amplitude excluded from calculations. This is pretty logical – near zero harmonics have phase undefined, almost pure noise.

PS
Another problem with extra low-resolution phase correlation is that sometimes motion vector appear not as primary, but as secondary peak, due to ambiguity of the images relations. I have yet to find out what to do in this situation…

Importance of phase

Here are some nice pictures illustrating importance of Fourier phase

Another prospective AR device

Nokia RX-51. It reported having OMAP3 600Mhz CPU with hardware 3D, camera, phone connectivity – it’s not a pure tablet like N800, GPS, accelerometers, and most testy – Maemo 5 Linux
No reports of electronic compass though.

Bundle Adjustemnt on the Mars with Rover

Just found out – Mars Rovers used bundle adjustment for its localization and rocks modeling:
“Purpose of algorithm:
To perform autonomous long-range rover localization based on bundle adjustment (BA) technology.
Processing steps of the algorithm include interest point extraction and matching, intra- and inter- stereo tie point selection, automatic cross-site tie point selection by rock extraction, modeling and matching, and bundle adjustment”