Fundamentals of digital image processing
Fundamentals of digital image processing
Adaptive Smoothing: A General Tool for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the feasibility of cross-validation in image analysis
SIAM Journal on Applied Mathematics
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Nonlinear image processing
Digital Picture Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Nonlinear Signal and Image Processing: Theory, Methods, and Applications
Nonlinear Signal and Image Processing: Theory, Methods, and Applications
All of Nonparametric Statistics (Springer Texts in Statistics)
All of Nonparametric Statistics (Springer Texts in Statistics)
On the recovery of a function on a circular domain
IEEE Transactions on Information Theory
Partition-based weighted sum filters for image restoration
IEEE Transactions on Image Processing
On the origin of the bilateral filter and ways to improve it
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Improving the Efficiency of Counting Defects by Learning RBF Nets with MAD Loss
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Information Theory Inspired Weighted Immune Classification Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
MAD Loss in Pattern Recognition and RBF Learning
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Sampling multidimensional signals by a new class of quasi-random sequences
Multidimensional Systems and Signal Processing
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A class of nonparametric smoothing kernel methods for image processing and filtering that possess edge-preserving properties is examined. The proposed approach is a nonlinearly modified version of the classical nonparametric regression estimates utilizing the concept of vertical weighting. The method unifies a number of known nonlinear image filtering and denoising algorithms such as bilateral and steering kernel filters. It is shown that vertically weighted filters can be realized by a structure of three interconnected radial basis function (RBF) networks. We also assess the performance of the algorithm by studying industrial images.