Smoothing with split linear fits
Technometrics
Discrete-time signal processing
Discrete-time signal processing
Periodogram with varying and data-driven window length
Signal Processing
Sensor array signal tracking using a data-driven window approach
Signal Processing
Adaptive Window Size Image De-noising Based on Intersection of Confidence Intervals (ICI) Rule
Journal of Mathematical Imaging and Vision
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Digital Step Edges from Zero Crossing of Second Directional Derivatives
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new method for varying adaptive bandwidth selection
IEEE Transactions on Signal Processing
Edge detection in untextured and textured images-a common computational framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonparametric algorithm for local frequency estimation of multidimensional signals
IEEE Transactions on Image Processing
Journal of Signal Processing Systems
Face recognition using scale-adaptive directional and textural features
Pattern Recognition
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In nonparametric local polynomial regression the adaptive selection of the scale parameter (window size/bandwidth) is a key problem. Recently new efficient algorithms, based on Lepski's approach, have been proposed in mathematical statistics for spatially adaptive varying scale denoising. A common feature of these algorithms is that they form test-estimates y@?"h different by the scale h@?H and special statistical rules are exploited in order to select the estimate with the best pointwise varying scale. In this paper a novel multiresolution (MR) local polynomial regression is proposed. Instead of selection of the estimate with the best scale h a nonlinear estimate is built using all of the test-estimates y@?"h. The adaptive estimation consists of two steps. The first step transforms the data into noisy spectrum coefficients (MR analysis). On the second step, this noisy spectrum is filtered by the thresholding procedure and used for estimation (MR synthesis).