A common viewpoint on broad kernel filtering and nonlinear diffusion

Authors:
Danny Barash;Dorin Comaniciu
Affiliations:
Department of Chemistry and Courant Institute of Mathematical Sciences, New York University and Howard Hughes Medical Institute, New York, NY;Real-Time Vision and Modeling Department, Siemens Corporate Research, Princeton, NJ
Venue:
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Year:
2003

Citing 18
Cited 4

Tree graphs of RNA secondary structures and their comparisons

Computers and Biomedical Research
Scale-Space and Edge Detection Using Anisotropic Diffusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Partitioning sparse matrices with eigenvectors of graphs

SIAM Journal on Matrix Analysis and Applications
Adaptive Smoothing: A General Tool for Early Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms

Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast bilateral filtering for the display of high-dynamic-range images

Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A Fundamental Relationship between Bilateral Filtering, Adaptive Smoothing, and the Nonlinear Diffusion Equation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusions and Confusions in Signal and Image Processing

Journal of Mathematical Imaging and Vision
The Structure of Locally Orderless Images

International Journal of Computer Vision
An Algorithm for Data-Driven Bandwidth Selection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift Analysis and Applications

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Bilateral Filtering for Gray and Color Images

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A general framework for low level vision

IEEE Transactions on Image Processing
Efficient and reliable schemes for nonlinear diffusion filtering

IEEE Transactions on Image Processing
Robust anisotropic diffusion

IEEE Transactions on Image Processing
The digital TV filter and nonlinear denoising

IEEE Transactions on Image Processing
On the origin of the bilateral filter and ways to improve it

IEEE Transactions on Image Processing

Spectral Decomposition of the Laplacian Matrix Applied to RNA Folding Prediction

CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Nonlinear diffusion filtering on extended neighborhood

Applied Numerical Mathematics
Structural analysis of single-point mutations given an RNA sequence: a case study with RNAMute

EURASIP Journal on Applied Signal Processing
Fast 3D mean shift filter for CT images

SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

Using a consistent adaptive smoothing formulation we show that both nonlinear diffusion and adaptive smoothing can be extended to an arbitrary window, a process called broad kernel filtering. Based on this idea, this paper presents a unified treatment of a number of well known nonlinear techniques for filtering. We show that bilateral filtering represents a particular choice of weights in the extended diffusion process, that is obtained from geometrical considerations. We then show that kernel density estimation applied in the joint spatial-range domain yields a powerful processing paradigm - the mean shift procedure, related to bilateral filtering but having additional flexibility. This establishes an attractive relationship between the theory of statistics and that of diffusion and energy minimization. We experimentally compare the discussed methods and give insights on their performance.