2,886 research outputs found
Learning sparse representations of depth
This paper introduces a new method for learning and inferring sparse
representations of depth (disparity) maps. The proposed algorithm relaxes the
usual assumption of the stationary noise model in sparse coding. This enables
learning from data corrupted with spatially varying noise or uncertainty,
typically obtained by laser range scanners or structured light depth cameras.
Sparse representations are learned from the Middlebury database disparity maps
and then exploited in a two-layer graphical model for inferring depth from
stereo, by including a sparsity prior on the learned features. Since they
capture higher-order dependencies in the depth structure, these priors can
complement smoothness priors commonly used in depth inference based on Markov
Random Field (MRF) models. Inference on the proposed graph is achieved using an
alternating iterative optimization technique, where the first layer is solved
using an existing MRF-based stereo matching algorithm, then held fixed as the
second layer is solved using the proposed non-stationary sparse coding
algorithm. This leads to a general method for improving solutions of state of
the art MRF-based depth estimation algorithms. Our experimental results first
show that depth inference using learned representations leads to state of the
art denoising of depth maps obtained from laser range scanners and a time of
flight camera. Furthermore, we show that adding sparse priors improves the
results of two depth estimation methods: the classical graph cut algorithm by
Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page
Synthesis of neural networks for spatio-temporal spike pattern recognition and processing
The advent of large scale neural computational platforms has highlighted the
lack of algorithms for synthesis of neural structures to perform predefined
cognitive tasks. The Neural Engineering Framework offers one such synthesis,
but it is most effective for a spike rate representation of neural information,
and it requires a large number of neurons to implement simple functions. We
describe a neural network synthesis method that generates synaptic connectivity
for neurons which process time-encoded neural signals, and which makes very
sparse use of neurons. The method allows the user to specify, arbitrarily,
neuronal characteristics such as axonal and dendritic delays, and synaptic
transfer functions, and then solves for the optimal input-output relationship
using computed dendritic weights. The method may be used for batch or online
learning and has an extremely fast optimization process. We demonstrate its use
in generating a network to recognize speech which is sparsely encoded as spike
times.Comment: In submission to Frontiers in Neuromorphic Engineerin
Three real-space discretization techniques in electronic structure calculations
A characteristic feature of the state-of-the-art of real-space methods in
electronic structure calculations is the diversity of the techniques used in
the discretization of the relevant partial differential equations. In this
context, the main approaches include finite-difference methods, various types
of finite-elements and wavelets. This paper reports on the results of several
code development projects that approach problems related to the electronic
structure using these three different discretization methods. We review the
ideas behind these methods, give examples of their applications, and discuss
their similarities and differences.Comment: 39 pages, 10 figures, accepted to a special issue of "physica status
solidi (b) - basic solid state physics" devoted to the CECAM workshop "State
of the art developments and perspectives of real-space electronic structure
techniques in condensed matter and molecular physics". v2: Minor stylistic
and typographical changes, partly inspired by referee comment
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