An Improved Adaptive Smoothing Method

Authors:
Xin Hu;Hui Peng;Joseph Kesker;Xiang Cai;William G. Wee;Jing-Huei Lee
Affiliations:
Microsoft Corporation, Redmond, Washington, United States 98052;Advantest America, INC, Santa Clara, United States 95054;Sheet Dynamic Ltd., Cincinnati, United States 45212;Department of Electrical and Computer Engineering, University of Cincinnati, Cincinnati, United States 45221;Department of Electrical and Computer Engineering, University of Cincinnati, Cincinnati, United States 45221;Department of Biomedical Engineering, University of Cincinnati, Cincinnati, United States 45221
Venue:
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Year:
2009

Citing 10
Cited 0

Scale-Space and Edge Detection Using Anisotropic Diffusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation

Graphical Models and Image Processing
Scale-based fuzzy connected image segmentation: theory, algorithms, and validation

Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Multiseeded Segmentation Using Fuzzy Connectedness

IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimum Image Thresholding via Class Uncertainty and Region Homogeneity

IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing

Digital Image Processing
Adaptive Smoothing via Contextual and Local Discontinuities

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy spatial relationships for image processing and interpretation: a review

Image and Vision Computing
Robust anisotropic diffusion

IEEE Transactions on Image Processing
Image segmentation based on fuzzy connectedness using dynamic weights

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

An improvement of the Chen's method has been provided through the calculation of a more accurate H map. The H map is the pixel's contextual inhomogeneity value reflecting its proximity position with respect to an edge feature, and a more accurate H value leads to the more accurate smoothing speed for the pixel. While experiments on 5 real images show slight improvements in SNRs of our method over that of the Chen method, edge features preserving capability has been enhanced with low FARs (false alarm rates) for edge feature extracted from applying the Sobel filter to the image. Furthermore, parameter values have been determined through an exhaustive searching process resulting in the suggestions of h=0.4 and T=4 for practical applications where the original noise free image is not available and/or no viewer to visually make a selection of the final smoothed image as the output.