1,965 research outputs found
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
Selective attention and speech processing in the cortex
In noisy and complex environments, human listeners must segregate the mixture of sound sources arriving at their ears and selectively attend a single source, thereby solving a computationally difficult problem called the cocktail party problem. However, the neural mechanisms underlying these computations are still largely a mystery. Oscillatory synchronization of neuronal activity between cortical areas is thought to provide a crucial role in facilitating information transmission between spatially separated populations of neurons, enabling the formation of functional networks.
In this thesis, we seek to analyze and model the functional neuronal networks underlying attention to speech stimuli and find that the Frontal Eye Fields play a central 'hub' role in the auditory spatial attention network in a cocktail party experiment. We use magnetoencephalography (MEG) to measure neural signals with high temporal precision, while sampling from the whole cortex. However, several methodological issues arise when undertaking functional connectivity analysis with MEG data. Specifically, volume conduction of electrical and magnetic fields in the brain complicates interpretation of results. We compare several approaches through simulations, and analyze the trade-offs among various measures of neural phase-locking in the presence of volume conduction. We use these insights to study functional networks in a cocktail party experiment.
We then construct a linear dynamical system model of neural responses to ongoing speech. Using this model, we are able to correctly predict which of two speakers is being attended by a listener. We then apply this model to data from a task where people were attending to stories with synchronous and scrambled videos of the speakers' faces to explore how the presence of visual information modifies the underlying neuronal mechanisms of speech perception. This model allows us to probe neural processes as subjects listen to long stimuli, without the need for a trial-based experimental design. We model the neural activity with latent states, and model the neural noise spectrum and functional connectivity with multivariate autoregressive dynamics, along with impulse responses for external stimulus processing. We also develop a new regularized Expectation-Maximization (EM) algorithm to fit this model to electroencephalography (EEG) data
Graph Laplacian for Image Anomaly Detection
Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image
anomaly detection; however, it presents known limitations, namely the
dependence over the image following a multivariate Gaussian model, the
estimation and inversion of a high-dimensional covariance matrix, and the
inability to effectively include spatial awareness in its evaluation. In this
work, a novel graph-based solution to the image anomaly detection problem is
proposed; leveraging the graph Fourier transform, we are able to overcome some
of RXD's limitations while reducing computational cost at the same time. Tests
over both hyperspectral and medical images, using both synthetic and real
anomalies, prove the proposed technique is able to obtain significant gains
over performance by other algorithms in the state of the art.Comment: Published in Machine Vision and Applications (Springer
Computational auditory scene analysis exploiting speech-recognition knowledge
The field of computational auditory scene analysis (CASA) strives to build computer models of the human ability to interpret sound mixtures as the combination of distinct sources. A major obstacle to this enterprise is defining and incorporating the kind of high level knowledge of real-world signal structure exploited by listeners. Speech recognition, while typically ignoring the problem of nonspeech inclusions, has been very successful at deriving powerful statistical models of speech structure from training data. In this paper, we describe a scene analysis system that includes both speech and nonspeech components, addressing the problem of working backwards from speech recognizer output to estimate the speech component of a mixture. Ultimately, such hybrid approaches will require more radical adaptation of current speech recognition approaches
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