Image segmentation in a kernel-induced space

Authors:
M. Ben Salah;A. Mitiche;I Ben Ayed
Affiliations:
EMT, Institut national de la recherche scientifique, Montréal, QC, Canada;EMT, Institut national de la recherche scientifique, Montréal, QC, Canada;General Electric Canada, London, ON, Canada
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 11
Cited 0

Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Influence of the Noise Model on Level Set Active Contour Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiregion Level-Set Partitioning of Synthetic Aperture Radar Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiregion competition: a level set extension of region competition to multiple region image partitioning

Computer Vision and Image Understanding
Polarimetric Image Segmentation via Maximum-Likelihood Approximation and Efficient Multiphase Level-Sets

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical modeling and conceptualization of visual patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active contours without edges

IEEE Transactions on Image Processing
Minimal Stochastic Complexity Image Partitioning With Unknown Noise Model

IEEE Transactions on Image Processing
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel level set multiphase image segmentation method combined with kernel mapping is presented. A kernel function maps implicitly the original data into data of a higher dimension so that the piecewise constant model becomes applicable. The goal is to consider several types of noise by a single model. Gradient flow equations are iteratively derived in order to minimize the segmentation functional with respect to the partition, in a first step, and the regions parameters in a second step. Using a common kernel function, we verified the effectiveness of the method by a quantitative and comparative performance evaluation over experiments on synthetic images, as well as a variety of real images such as medical, SAR, and natural images.