Computation of Ridges via Pullback Metrics from Scale Space

1999 ◽  
pp. 82-92 ◽  
Author(s):  
Michael Kerckhove
Keyword(s):  
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.

scholarly journals Real-time FPGA-based implementation of the AKAZE algorithm with nonlinear scale space generation using image partitioning

2021 ◽  
Author(s):  
Parastoo Soleimani ◽  
David W. Capson ◽  
Kin Fun Li
Keyword(s):  
Real Time ◽  
Memory Management ◽  
Scale Space ◽  
Image Resolution ◽  
Hardware Architecture ◽  
Frame Rate ◽  
Time Sharing ◽  
Scale Invariant ◽  
Nonlinear Scale Space ◽  
Nonlinear Scale

AbstractThe first step in a scale invariant image matching system is scale space generation. Nonlinear scale space generation algorithms such as AKAZE, reduce noise and distortion in different scales while retaining the borders and key-points of the image. An FPGA-based hardware architecture for AKAZE nonlinear scale space generation is proposed to speed up this algorithm for real-time applications. The three contributions of this work are (1) mapping the two passes of the AKAZE algorithm onto a hardware architecture that realizes parallel processing of multiple sections, (2) multi-scale line buffers which can be used for different scales, and (3) a time-sharing mechanism in the memory management unit to process multiple sections of the image in parallel. We propose a time-sharing mechanism for memory management to prevent artifacts as a result of separating the process of image partitioning. We also use approximations in the algorithm to make hardware implementation more efficient while maintaining the repeatability of the detection. A frame rate of 304 frames per second for a $$1280 \times 768$$ 1280 × 768 image resolution is achieved which is favorably faster in comparison with other work.

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