A Note on Local Morse Theory in Scale Space and Gaussian Deformations

2005 ◽  
pp. 249-258
Author(s):  
Jan–Cees van der Meer
Keyword(s):  
Morse Theory ◽  
Scale Space

NeighShrink Despeckling for SAR Images Based on Scale Space Correlation

2011 ◽  
Vol 30 (8) ◽  
pp. 1940-1943
Author(s):  
Heng-chao Li ◽  
Wen Hong ◽  
Yi-rong Wu
Keyword(s):  
Scale Space ◽  
Sar Images ◽  
Space Correlation

Capture Strategy Based on a Distributed Control System for a Large-scale Space End-Effector

ROBOT ◽  
2011 ◽  
Vol 33 (4) ◽  
pp. 434-439 ◽  
Author(s):  
Dangyang JIE ◽  
Fenglei NI ◽  
Yisong TAN ◽  
Hong LIU ◽  
Hegao CAI
Keyword(s):  
Control System ◽  
Distributed Control ◽  
Large Scale ◽  
Scale Space ◽  
Distributed Control System ◽  
End Effector

scholarly journals Bayesian modeling and significant features exploration in wavelet power spectra

2007 ◽  
Vol 14 (1) ◽  
pp. 79-88 ◽  
Author(s):  
D. V. Divine ◽  
F. Godtliebsen
Keyword(s):  
Data Analysis ◽  
Traditional Approach ◽  
Statistical Significance ◽  
Power Spectra ◽  
Simulated Data ◽  
Scale Space ◽  
Independent Random Variables ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Testing Procedures

Abstract. This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes' rule for constructing the posterior distribution of the smoothed wavelet coefficients. Samples drawn from the posterior are subsequently used for finding the estimate of the true wavelet spectrum at each scale. The method offers two different significance testing procedures for wavelet spectra. A traditional approach assesses the statistical significance against a red noise background. The second procedure tests for homoscedasticity of the wavelet power assessing whether the spectrum derivative significantly differs from zero at each particular point of the spectrum. Case studies with simulated data and climatic time-series prove the method to be a potentially useful tool in data analysis.

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