5,382 research outputs found
Multiscale Discriminant Saliency for Visual Attention
The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between center and surround
classes. Discriminant power of features for the classification is measured as
mutual information between features and two classes distribution. The estimated
discrepancy of two feature classes very much depends on considered scale
levels; then, multi-scale structure and discriminant power are integrated by
employing discrete wavelet features and Hidden markov tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, saliency value for
each dyadic square at each scale level is computed with discriminant power
principle and the MAP. Finally, across multiple scales is integrated the final
saliency map by an information maximization rule. Both standard quantitative
tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating
the proposed multiscale discriminant saliency method (MDIS) against the
well-know information-based saliency method AIM on its Bruce Database wity
eye-tracking data. Simulation results are presented and analyzed to verify the
validity of MDIS as well as point out its disadvantages for further research
direction.Comment: 16 pages, ICCSA 2013 - BIOCA sessio
Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling
The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between centre and surround
classes. Discriminant power of features for the classification is measured as
mutual information between distributions of image features and corresponding
classes . As the estimated discrepancy very much depends on considered scale
level, multi-scale structure and discriminant power are integrated by employing
discrete wavelet features and Hidden Markov Tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, a saliency value for
each square block at each scale level is computed with discriminant power
principle. Finally, across multiple scales is integrated the final saliency map
by an information maximization rule. Both standard quantitative tools such as
NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed
multi-scale discriminant saliency (MDIS) method against the well-know
information based approach AIM on its released image collection with
eye-tracking data. Simulation results are presented and analysed to verify the
validity of MDIS as well as point out its limitation for further research
direction.Comment: arXiv admin note: substantial text overlap with arXiv:1301.396
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction
Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into
time series representing neurophysiological activity in fixed frequency bands.
Using these time series, one can estimate frequency-band specific functional
connectivity between sensors or regions of interest, and thereby construct
functional brain networks that can be examined from a graph theoretic
perspective. Despite their common use, however, practical guidelines for the
choice of wavelet method, filter, and length have remained largely
undelineated. Here, we explicitly explore the effects of wavelet method (MODWT
vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least
Asymmetric, and Coiflet families), and wavelet length (2 to 24) - each
essential parameters in wavelet-based methods - on the estimated values of
network diagnostics and in their sensitivity to alterations in psychiatric
disease. We observe that the MODWT method produces less variable estimates than
the DWT method. We also observe that the length of the wavelet filter chosen
has a greater impact on the estimated values of network diagnostics than the
type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of
the method to detect differences between health and disease and tunes
classification accuracy. Collectively, our results suggest that the choice of
wavelet method and length significantly alters the reliability and sensitivity
of these methods in estimating values of network diagnostics drawn from graph
theory. They furthermore demonstrate the importance of reporting the choices
utilized in neuroimaging studies and support the utility of exploring wavelet
parameters to maximize classification accuracy in the development of biomarkers
of psychiatric disease and neurological disorders.Comment: working pape
Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis
© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.Peer reviewedFinal Accepted Versio
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