490 research outputs found
Classification of Occluded Objects using Fast Recurrent Processing
Recurrent neural networks are powerful tools for handling incomplete data
problems in computer vision, thanks to their significant generative
capabilities. However, the computational demand for these algorithms is too
high to work in real time, without specialized hardware or software solutions.
In this paper, we propose a framework for augmenting recurrent processing
capabilities into a feedforward network without sacrificing much from
computational efficiency. We assume a mixture model and generate samples of the
last hidden layer according to the class decisions of the output layer, modify
the hidden layer activity using the samples, and propagate to lower layers. For
visual occlusion problem, the iterative procedure emulates feedforward-feedback
loop, filling-in the missing hidden layer activity with meaningful
representations. The proposed algorithm is tested on a widely used dataset, and
shown to achieve 2 improvement in classification accuracy for occluded
objects. When compared to Restricted Boltzmann Machines, our algorithm shows
superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author
Factor Graph Based LMMSE Filtering for Colored Gaussian Processes
We propose a low complexity, graph based linear minimum mean square error
(LMMSE) filter in which the non-white characteristics of a random process are
taken into account. Our method corresponds to block LMMSE filtering, and has
the advantage of complexity linearly increasing with the block length and the
ease of incorporating the a priori information of the input signals whenever
possible. The proposed method can be used with any random process with a known
autocorrelation function with the help of an approximation to an autoregressive
(AR) process. We show through extensive simulations that our method performs
very close to the optimal block LMMSE filtering for Gaussian input signals.Comment: 5 pages, 4 figure
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