Valchava workshop, day two

In the morning, Schuler, Harmeling, Hirsch, Schölkopf, Hormuth, and I started the necessary work to convert some of Schuler and Hirsch's blind deconvolution code over into a package that could take an arbitrary astronomical image and return a spatially varying point-spread function. We also, on the side, struggled with downloading and compiling Astrometry.net code. Schölkopf was surprised to hear that we don't have funding for that project!

In the afternoon, Dilip Krishnan (NYU) talked about his work with Fergus to remove rain and other kinds of complex, heterogeneous occlusions from photographs. They are using a discriminative (rather than a generative) approach, which in this case means that they train with data as close as possible to the data that they want to fix. That sets up some ideas for my talk tomorrow. There was also discussion of his and Fergus's very clever priors on image gradients in natural images: There are some 0.8 exponents that amuse me.

Felix Hormuth (MPIA) talked about the lucky imaging camera AstraLux, which he built and has been very successful and was incredibly cheap to build. He showed the data and gave the crowd some ideas about how astronomical seeing arises. He also showed some data sets that do not properly reduce under the standard, vanilla lucky-imaging-style pipelines. He distributed data to the interested parties.

Sam Hasinoff (Google) continued the craziness of yesterday, talking about inferring a scene from the shadows and reflections it creates in illuminated objects. Lambertian objects (objects that don't produce specular reflections) are not very informative when they are convex, but become valuable when their shapes get more complex. He showed some very nice results reconstructing Mars from single-pixel photometry data, which are very related to recent exoplanet studies with Spitzer.

Krik Muandet (Tübingen) showed support measure machines, which are a generalization of SVMs attempting to deal with the idea that the data might in fact be probability distributions rather than samples. He is trying to improve upon XDQSO target selection. Tomorrow I am going to show him how to test his results using the just-released SDSS-III DR9 Data