New year in life and new job

A new year in life started a couple of days ago. Sharing the birthday with Einstein brings some disadvantages and advantages at the same time. One of the advantages is the constant nagging urge to learn more about the universe and see it the way the my illustrious birthdaymate did. The disadvantage is realising that if I were ever to invent a time machine and move in this extra dimension he conjured up, I will have to keep looking for clues that I have left myself - damn you predestination paradox.

As part of my continuous Brownian motion through life, I started a new job. No organisation (organism) is cool without a scientific name or acronym, the previous one I worked for was a mouthful - CSIRO, the current one is shorter, just AMX (Aerometrex). Doing multiview geometry mapping and point cloud collection. Calibrating lots of cameras in Agisoft, Imageiron, PhotoModeler etc. The approach here is very pragmatic, we will go with whatever is available off the shelf to create the product and my job is to develop an efficient production chain using the right mix of automation and human intervention.

The first order of business was setting up the development environment including - Python 2.7 64bit goodies and implementing Python image calibration (with some changes to account for the new cv2 API which makes numpy arrays and OpenCV images identical). I read through Zhang's core paper on simple flat checkerboard based camera calibration, implemented in OpenCV to estimate 2-3 radial distortion parameters and 2 tangential parameters (Brown's model), as well as the X and Y focal lengths and principal point (which can be different if the lens has astigmatism). Staring at these calibration targets for a while tends to give you optical illusions as the eye and the brain aim to iteratively approach a calibrated view of reality.



OpenCV needs to be told how many corners to expect, so a simple histogram equalisation and mean transition count is required on the checkerboard. Then simply populate the camera ( remember the corner element is 1) and distortions matrix and undistort. Writing calibrations and undistorts with higher order polynomials and even piecewise linear functions will be required for wide angle lenses. Otherwise I am also looking at the 3 rotations degrees of freedom we have in spaceland and their methods of representation via Rotation matrixes, Quaternions and Euler angles. Is there rotational degree of freedom in space-time land ?

Fusing channel - array of cheap camera

I decided to do another experiment with my multitude of USB webcams and Gstreamer. This time I am capturing 2 shots using the stereo config I used before, but with an InfraRed filter with 850nm as the pass-band over one of the cameras. The rig looks nothing spectacular but costs only $40 net.


There is some parallax and neither the camera nor the mount is calibrated. Registration of the multi-band images is purely by trial and error at this stage. I have written it up with matplotlib, numpy and PIL to allow more automated transform estimation.


The overlapped section is cropped using array subsetting and channels are reassigned as InfraRed , Red , Green instead of plain RGB to form a False Colour Composite. Some of the misaligned sections can be seen highlighted. I also have 950nm and 750nm filters to test more detail in the spectral response. The compositing will obviously become harder and harder as a manual process till I build a stable mount, but I kind of like the extensibility with clip-on-mounting.

The solution is to use the well developed feature based image matching techniques and let feature descriptors like SURF, SIFT or Harris Corners pick up the bits I picked up by hand. The spectral variation provides additional challenge.

I finish off with the composite built so far.  Low costs systems like this can be used for environmental studies where lugging large cameras and spectrometers around rough terrain is not pleasant.

The FOSS4G gang comes out on Haiti

 The group of people who helped put together aerial after Katrina have started a set of web services to deliver the situational imagery being collected by the American and any other satellites and aerial platforms over Haiti.

The anchor site is telascience, the quick access url's to Openlayers viewers for imagery and WMS access from other viewers are here and here.

Other satellite imagery from EROS can be viewed here.

Nearmap - Dense observation of space-time

Nearmap has finally had its public data release. Nice slippy maps view and you would expect in any web 2.0 mapping site. The biggest difference is you can make the time slip in addition to space - every capture date has a separate set of vertical and oblique views as well as separate stereo elevation model.

As with any large automated system e.g. Google the onus is on the users to find anomalies and QA the huge volume of data.

 

There are indeed some anomalies in the non-vertical modes over Adelaide. I hope they will take user feedback and improve the processing flow fulfill the great promise of recent aerial, elevation and city models this system holds.