Wednesday, April 09, 2008

Compressed Sensing: a blog, Autonomous geometric precision error estimation in low-level computer vision tasks, Neuroscience Datasets

3D rendering of a DEM used for the topography of MarsImage via Wikipedia
I just recently found a blog that seems to focus on Compressed Sensing. In all, I have to say that Google is not doing a great job at taking into account labels from its own platform. In particular, I have found some odd discrepancies between the pagerank of some pages and the number of people that use Google reader to view them. Anyway, the blog of Andrés Corrada-Emmanuel entitled De Rerum Natura brings up some interesting applications in the determination of elevation models (DEM) from photographs as he has to deal with underdetermined systems of equations.. The postings are at:

he just had his paper accepted at ICML 08. The title is Autonomous geometric precision error estimation in low-level computer vision tasks by Andrés Corrada-Emmanuel and Howard Schultz. The abstract reads:

Errors in map-making tasks using computer vision are sparse. We demonstrate this by considering the construction of digital elevation models that employ stereo matching algorithms to triangulate real-world points. This sparsity, coupled with a geometric theory of errors recently developed by the authors, allows for autonomous agents to calculate their own precision independently of ground truth. We connect these developments with recent advances in the mathematics of sparse signal reconstruction or compressed sensing. The theory presented here extends the autonomy of 3-D model reconstructions discovered in the 1990s to their errors.

This is new. I don't think I have ever seen anybody talking about precision errors being a sparse signals before.

With the Netflix competition, we saw a lot of very interesting developments. One of them, as Andrew Gelman points out, is the fact that with a flurry of algorithms to choose from, data is paramount in leading to new and improved findings. An example of something similar in Neuroscience is the neuron challenge mentioned before. However, as echoed in this entry by Hal Daume III ( Those Darn Biologists...) there is very little incentive for biologists to publish their results with an analysis using new algorithms: This is not getting their papers published. A new initiative may help in that respect: the CRCNS - Collaborative Research in Computational Neuroscience - Data sharing activity is making biological / neuroscience datasets available for download here. This is prodigious idea. It currently lists datasets in:

While The following additional data sets will be available by about June 2008:
  • V4 responses to synthetic parametric stimuli. (From Jack Gallant lab, UC Berkeley).
  • Responses in areas V1, V2 and V4 using precisely the same stimuli. Data collected to facilitate functional comparisons across successive stages of sensory processing. (From Jack Gallant lab, UC Berkeley).
  • Tutorial on understanding intracellular recordings in sensory areas and accompanying data. The data are intracellular (whole-cell patch) recordings obtained in vivo from visual, auditory, somatosensory, and motor areas of the neocortex by the laboratories of Judith Hirsch, USC; Anthony Zador, CSHL; Michael DeWeese, UC Berkeley and Michael Brecht, Humboldt University Berlin. These data include not only spikes but also membrane voltages or currents generated by synaptic connections and intrinsic membrane channels.
  • Synaptic plasticity data – Cortical slice data acquired in order to examine the effects of complex spike trains in the induction of long-term synaptic modification and recordings of primary visual cortical neurons made during stimulation. (From Yang Dan lab, UC Berkeley).
  • Recordings from hippocampal CA1 neurons during open field foraging. (From Buzsáki lab, Rutgers University).

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