4,729 research outputs found
A new self-organizing neural gas model based on Bregman divergences
In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural
Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization
capabilities.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec
Variational Bayesian algorithm for quantized compressed sensing
Compressed sensing (CS) is on recovery of high dimensional signals from their
low dimensional linear measurements under a sparsity prior and digital
quantization of the measurement data is inevitable in practical implementation
of CS algorithms. In the existing literature, the quantization error is modeled
typically as additive noise and the multi-bit and 1-bit quantized CS problems
are dealt with separately using different treatments and procedures. In this
paper, a novel variational Bayesian inference based CS algorithm is presented,
which unifies the multi- and 1-bit CS processing and is applicable to various
cases of noiseless/noisy environment and unsaturated/saturated quantizer. By
decoupling the quantization error from the measurement noise, the quantization
error is modeled as a random variable and estimated jointly with the signal
being recovered. Such a novel characterization of the quantization error
results in superior performance of the algorithm which is demonstrated by
extensive simulations in comparison with state-of-the-art methods for both
multi-bit and 1-bit CS problems.Comment: Accepted by IEEE Trans. Signal Processing. 10 pages, 6 figure
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