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SVM for edge-preserving filtering
Qingxiong Yang
, Shengnan Wang
,
Narendra Ahuja
National Center for Supercomputing Applications (NCSA)
Electrical and Computer Engineering
Siebel School of Computing and Data Science
Research output
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Keyphrases
Edge Filter
100%
Support Vector Machine
100%
Bilateral Filter
100%
Filter Method
33%
Feature Vector
33%
Variance Value
33%
Exponentiation
33%
Learning-based
16%
Computational Complexity
16%
Spatial Filtering
16%
Mapping Function
16%
Relative Difference
16%
Weight Function
16%
Filter Kernel
16%
Pixel Intensity
16%
Filter Length
16%
Least Squares Support Vector Regression (LSSVR)
16%
Spatial Distance
16%
Value-based
16%
High Range
16%
Filtering Problems
16%
Original Image
16%
Training Image
16%
Over-smoothing
16%
Intensity Range
16%
Over-under
16%
Spatial Weighting
16%
Normalized Convolution
16%
Range Weighting
16%
Vector Mapping
16%
Center pixel
16%
Mathematics
Edge
100%
Variance
100%
Support Vector Machine
100%
Gaussian Distribution
66%
Exponentiation
66%
Feature Vector
66%
Weighting Functions
33%
Convolution
33%
Filtering Problem
33%
Training Image
33%
Computer Science
Support Vector Machine
100%
Filtering Method
66%
Feature Vector
66%
Approximation (Algorithm)
33%
Computational Complexity
33%
Mapping Function
33%
Training Image
33%
Weighting Functions
33%
Pixel Intensity
33%
Relative Difference
33%
Engineering
Support Vector Machine
100%
Gaussians
66%
Feature Vector
66%
Computational Complexity
33%
Mapping Function
33%
Relative Difference
33%
Mapping Vector
33%