a crazy weekend today, vision project took a day, for extracting camera parameters from 3d points matches with their 2D projections on the camera image, and a cooler process of using 10 frames of matched points to extract both camera parameters and estimate the 3d point locations from the 10 2D images.

It’s pretty cool, but only the core. There are lots of other problems in practical use, including camera calibration (distortion, amorphic etc), and how to create point correspondence.

Finally, we reach unsupervised learning in our ‘machine’ class. I think it’s a class of algorithms more practical, but maybe less accurate? Unless there’s an automated way to collect databases of output labels, unsupervised should take the bulk of processing. So 2.5 days of K means clustering, and expectation maximization algorithm (EM) using gaussian mixtures. The obvious limitation is K is manually set, but there are methods on efficiently searching the optimal K prototypes in a 0…Infinity space, though I doubt that maximization can reach too high a number. It will become like finding the K best features, and ignore the rest that might be noise.