Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing
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Các bài báo tiêu biểu
Signal reconstruction from sampled data using neural network
- Trang 707-715
For reconstructing a signal from sampling data, the method based on Shannon's
sampling theorem is usually employed. The reconstruction error appears when the
signal does not satisfy the Nyquist condition. This paper proposes a new
reconstruction method by using a linear perceptron and multilayer perceptron as
FIR filter. The perceptron, which has weights obtained by learning when adapting
the orig...
#Signal reconstruction #Neural networks #Image reconstruction #Sampling methods #Finite impulse response filter #Frequency #Adaptive filters #Image sampling #Information retrieval #Physics
Feature selection for off-line recognition of different size signatures
- Trang 355-364
The aim of this work is to select a set of features, which have good performance
to solve the problem of signature recognition of different sizes. The signature
database was formed for three sizes of signatures per user, small, median and
big. This study uses structural features, pseudo-dynamic features and five
moment groups. The feature selection method chosen is the one that select the
best ind...
#Spatial databases #Handwriting recognition #Feature extraction #Data analysis #Data mining #Cameras #Forgery
Facial expression analysis using shape and motion information extracted by convolutional neural networks
- Trang 607-616
We discuss a neural networks-based face analysis approach that is able to cope
with faces subject to pose and lighting variations. Especially head pose
variations are difficult to tackle and many face analysis methods require the
use of sophisticated normalization procedures. Data-driven shape and
motion-based face analysis approaches are introduced that are not only capable
of extracting features...
#Information analysis #Shape #Motion analysis #Data mining #Neural networks #Feature extraction #Multi-layer neural network #Cellular neural networks #Data analysis #Face recognition
Fusion of multiple experts in multimodal biometric personal identity verification systems
- Trang 3-12
We investigate two trainable methods of classifier fusion in the context of
multimodal personal identity verification involving eight experts which exploit
voice characteristics and frontal face biometrics. As baseline classifier
combination methods, simple fusion rules (Sum and Vote) which do not require any
training are used. The results of experiments on the XM2VTS database show that
all four c...
#Biometrics #Fingers #Data security #Iris #Cameras #Speech processing #Biomedical signal processing #Face detection #Voting #Surveillance
Non-negative sparse coding
- Trang 557-565
Non-negative sparse coding is a method for decomposing multivariate data into
non-negative sparse components. We briefly describe the motivation behind this
type of data representation and its relation to standard sparse coding and
non-negative matrix factorization. We then give a simple yet efficient
multiplicative algorithm for finding the optimal values of the hidden
components. In addition, we...
#Independent component analysis #Sparse matrices #Vectors #Matrix decomposition #Signal processing algorithms #Data analysis #Signal analysis #Signal representations #Wavelet analysis #Statistics
Blind identification problems with constraints
- Trang 535-544
In many applications of independent component analysis (ICA) and blind source
separation (BSS) the mixing or separating matrices have some special structure
or some constraints are imposed for the matrices like symmetry, orthogonality,
nonnegativity, sparseness and unit (or specified invariant norm) of the matrix.
We present several algorithms and overview some known transformations which
allows u...
#Independent component analysis #Symmetric matrices #Source separation #Covariance matrix #Blind source separation #Signal processing algorithms #Infrared sensors #Matrix decomposition #Laboratories #Biomedical signal processing
Classification and ICA using maximum likelihood Hebbian learning
- Trang 327-336
We investigate an extension of Hebbian learning in a principal component
analysis network which has been derived to be optimal for a specific probability
density function(PDF). We note that this probability density function is one of
a family of PDFs and investigate the learning rules formed in order to be
optimal for several members of this family. We show that, whereas previous
authors have view...
#Independent component analysis #Hebbian theory #Principal component analysis #Neurons #Artificial neural networks #Negative feedback #Nonlinear equations #Computational intelligence #Probability density function #Mean square error methods
Face recognition using kernel principal component analysis and genetic algorithms
- Trang 337-343
Kernel principal component analysis (KPCA) as a powerful nonlinear feature
extraction method has proven as a preprocessing step for classification
algorithm. A face recognition approach based on KPCA and genetic algorithms
(GAs) is proposed. By the use of the polynomial functions as a kernel function
in KPCA, the high order relationships can be utilized and the nonlinear
principal components can b...
#Face recognition #Kernel #Principal component analysis #Genetic algorithms #Support vector machines #Support vector machine classification #Feature extraction #Classification algorithms #Polynomials #Spatial databases
Improvements on continuous unsupervised sleep staging
- Trang 687-695
We report improvements on automatic continuous sleep staging using hidden Markov
models (HMM). Contrary to our previous efforts, we trained the HMMs on data from
single sleep labs instead of generalizing to data from diverse sleep labs. Our
totally unsupervised approach detects the cornerstones of human sleep
(wakefulness, deep and rem sleep) with around 80% accuracy based on data from a
single EE...
#Sleep #Hidden Markov models #Electroencephalography #Electromyography #Humans #Electrooculography #Artificial intelligence #Brain modeling #Electrodes #Reflection
Neural network implementations of independent component analysis
- Trang 505-514
The performance of six neuromorphic adaptive structurally different algorithms
was analyzed in blind separation of independent artificially generated signals
using the stationary linear independent component analysis (ICA) model. The
estimated independent components were assessed and compared aiming to rank the
neural ICA implementations. All algorithms were run with different contrast
functions, ...
#Neural networks #Independent component analysis #Signal processing algorithms #Principal component analysis #Source separation #Array signal processing #Vectors #Artificial neural networks #Psychology #Neuromorphics